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-spambase
-Loading spambase (Spambase Data Set)
-#instances=4601, type=<class 'numpy.ndarray'>, #features=57, #classes=[0 1], prevs=[0.606, 0.394]
-<table border="1" class="dataframe">
-  <thead>
-    <tr>
-      <th></th>
-      <th colspan="2" halign="left">base</th>
-      <th colspan="4" halign="left">true</th>
-      <th colspan="4" halign="left">estim</th>
-      <th colspan="7" halign="left">errors</th>
-    </tr>
-    <tr>
-      <th></th>
-      <th>0</th>
-      <th>1</th>
-      <th>T0</th>
-      <th>F1</th>
-      <th>F0</th>
-      <th>T1</th>
-      <th>T0</th>
-      <th>F1</th>
-      <th>F0</th>
-      <th>T1</th>
-      <th>mae</th>
-      <th>rae</th>
-      <th>mrae</th>
-      <th>kld</th>
-      <th>nkld</th>
-      <th>f1e_true</th>
-      <th>f1e_estim</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>0.0000</td>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.1000</td>
-      <td>0.9000</td>
-      <td>0.0000</td>
-      <td>0.0038</td>
-      <td>0.0000</td>
-      <td>0.9962</td>
-      <td>0.0500</td>
-      <td>0.4571</td>
-      <td>0.4571</td>
-      <td>0.2254</td>
-      <td>0.1122</td>
-      <td>NaN</td>
-      <td>1.0000</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>0.0000</td>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.0800</td>
-      <td>0.9200</td>
-      <td>0.0000</td>
-      <td>0.0008</td>
-      <td>0.0000</td>
-      <td>0.9992</td>
-      <td>0.0400</td>
-      <td>0.2981</td>
-      <td>0.2981</td>
-      <td>0.1641</td>
-      <td>0.0819</td>
-      <td>NaN</td>
-      <td>0.9907</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>0.0000</td>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.1100</td>
-      <td>0.8900</td>
-      <td>0.0000</td>
-      <td>0.0012</td>
-      <td>0.0000</td>
-      <td>0.9988</td>
-      <td>0.0550</td>
-      <td>0.3289</td>
-      <td>0.3289</td>
-      <td>0.2568</td>
-      <td>0.1277</td>
-      <td>NaN</td>
-      <td>1.0000</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>0.0000</td>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.0900</td>
-      <td>0.9100</td>
-      <td>0.0000</td>
-      <td>0.0073</td>
-      <td>0.0000</td>
-      <td>0.9927</td>
-      <td>0.0450</td>
-      <td>0.6231</td>
-      <td>0.6231</td>
-      <td>0.1960</td>
-      <td>0.0977</td>
-      <td>NaN</td>
-      <td>0.9999</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>0.0000</td>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.1000</td>
-      <td>0.9000</td>
-      <td>0.0000</td>
-      <td>0.0178</td>
-      <td>0.0000</td>
-      <td>0.9822</td>
-      <td>0.0500</td>
-      <td>1.1522</td>
-      <td>1.1522</td>
-      <td>0.2334</td>
-      <td>0.1162</td>
-      <td>NaN</td>
-      <td>1.0000</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>0.0000</td>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.0600</td>
-      <td>0.9400</td>
-      <td>0.0000</td>
-      <td>0.0007</td>
-      <td>0.0000</td>
-      <td>0.9993</td>
-      <td>0.0300</td>
-      <td>0.2796</td>
-      <td>0.2796</td>
-      <td>0.1086</td>
-      <td>0.0542</td>
-      <td>NaN</td>
-      <td>1.0000</td>
-    </tr>
-    <tr>
-      <th>6</th>
-      <td>0.0000</td>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.1200</td>
-      <td>0.8800</td>
-      <td>0.0000</td>
-      <td>0.0008</td>
-      <td>0.0000</td>
-      <td>0.9992</td>
-      <td>0.0600</td>
-      <td>0.3150</td>
-      <td>0.3150</td>
-      <td>0.2898</td>
-      <td>0.1439</td>
-      <td>NaN</td>
-      <td>1.0000</td>
-    </tr>
-    <tr>
-      <th>7</th>
-      <td>0.0000</td>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.1100</td>
-      <td>0.8900</td>
-      <td>0.0000</td>
-      <td>0.0005</td>
-      <td>0.0000</td>
-      <td>0.9995</td>
-      <td>0.0550</td>
-      <td>0.2965</td>
-      <td>0.2965</td>
-      <td>0.2568</td>
-      <td>0.1277</td>
-      <td>NaN</td>
-      <td>1.0000</td>
-    </tr>
-    <tr>
-      <th>8</th>
-      <td>0.0000</td>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.0900</td>
-      <td>0.9100</td>
-      <td>0.0000</td>
-      <td>0.0009</td>
-      <td>0.0000</td>
-      <td>0.9991</td>
-      <td>0.0450</td>
-      <td>0.3055</td>
-      <td>0.3055</td>
-      <td>0.1939</td>
-      <td>0.0966</td>
-      <td>NaN</td>
-      <td>0.9761</td>
-    </tr>
-    <tr>
-      <th>9</th>
-      <td>0.0000</td>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.1400</td>
-      <td>0.8600</td>
-      <td>0.0000</td>
-      <td>0.0222</td>
-      <td>0.0000</td>
-      <td>0.9778</td>
-      <td>0.0700</td>
-      <td>1.3867</td>
-      <td>1.3867</td>
-      <td>0.3694</td>
-      <td>0.1826</td>
-      <td>NaN</td>
-      <td>1.0000</td>
-    </tr>
-    <tr>
-      <th>10</th>
-      <td>0.1000</td>
-      <td>0.9000</td>
-      <td>0.0900</td>
-      <td>0.0100</td>
-      <td>0.0800</td>
-      <td>0.8200</td>
-      <td>0.0085</td>
-      <td>0.2046</td>
-      <td>0.0000</td>
-      <td>0.7869</td>
-      <td>0.0973</td>
-      <td>3.7034</td>
-      <td>3.7034</td>
-      <td>0.4204</td>
-      <td>0.2072</td>
-      <td>0.3333</td>
-      <td>0.9233</td>
-    </tr>
-    <tr>
-      <th>11</th>
-      <td>0.1000</td>
-      <td>0.9000</td>
-      <td>0.1000</td>
-      <td>0.0000</td>
-      <td>0.1100</td>
-      <td>0.7900</td>
-      <td>0.0288</td>
-      <td>0.0769</td>
-      <td>0.0000</td>
-      <td>0.8943</td>
-      <td>0.0906</td>
-      <td>4.2863</td>
-      <td>4.2863</td>
-      <td>0.3676</td>
-      <td>0.1817</td>
-      <td>0.3548</td>
-      <td>0.5715</td>
-    </tr>
-    <tr>
-      <th>12</th>
-      <td>0.1000</td>
-      <td>0.9000</td>
-      <td>0.1000</td>
-      <td>0.0000</td>
-      <td>0.1000</td>
-      <td>0.8000</td>
-      <td>0.0649</td>
-      <td>0.0501</td>
-      <td>0.0000</td>
-      <td>0.8850</td>
-      <td>0.0675</td>
-      <td>2.8520</td>
-      <td>2.8520</td>
-      <td>0.2689</td>
-      <td>0.1337</td>
-      <td>0.3333</td>
-      <td>0.2786</td>
-    </tr>
-    <tr>
-      <th>13</th>
-      <td>0.1000</td>
-      <td>0.9000</td>
-      <td>0.1000</td>
-      <td>0.0000</td>
-      <td>0.0600</td>
-      <td>0.8400</td>
-      <td>0.0182</td>
-      <td>0.1656</td>
-      <td>0.0000</td>
-      <td>0.8161</td>
-      <td>0.0828</td>
-      <td>8.7145</td>
-      <td>8.7145</td>
-      <td>0.3317</td>
-      <td>0.1643</td>
-      <td>0.2308</td>
-      <td>0.8196</td>
-    </tr>
-    <tr>
-      <th>14</th>
-      <td>0.1000</td>
-      <td>0.9000</td>
-      <td>0.0900</td>
-      <td>0.0100</td>
-      <td>0.0800</td>
-      <td>0.8200</td>
-      <td>0.0243</td>
-      <td>0.1076</td>
-      <td>0.0000</td>
-      <td>0.8681</td>
-      <td>0.0728</td>
-      <td>2.0492</td>
-      <td>2.0492</td>
-      <td>0.2755</td>
-      <td>0.1369</td>
-      <td>0.3333</td>
-      <td>0.6885</td>
-    </tr>
-    <tr>
-      <th>15</th>
-      <td>0.1000</td>
-      <td>0.9000</td>
-      <td>0.1000</td>
-      <td>0.0000</td>
-      <td>0.0800</td>
-      <td>0.8200</td>
-      <td>0.0767</td>
-      <td>0.0219</td>
-      <td>0.0000</td>
-      <td>0.9015</td>
-      <td>0.0517</td>
-      <td>1.4088</td>
-      <td>1.4088</td>
-      <td>0.1811</td>
-      <td>0.0903</td>
-      <td>0.2857</td>
-      <td>0.1248</td>
-    </tr>
-    <tr>
-      <th>16</th>
-      <td>0.1000</td>
-      <td>0.9000</td>
-      <td>0.0800</td>
-      <td>0.0200</td>
-      <td>0.0700</td>
-      <td>0.8300</td>
-      <td>0.0176</td>
-      <td>0.0496</td>
-      <td>0.0001</td>
-      <td>0.9327</td>
-      <td>0.0662</td>
-      <td>0.7438</td>
-      <td>0.7438</td>
-      <td>0.1981</td>
-      <td>0.0987</td>
-      <td>0.3600</td>
-      <td>0.5856</td>
-    </tr>
-    <tr>
-      <th>17</th>
-      <td>0.1000</td>
-      <td>0.9000</td>
-      <td>0.0800</td>
-      <td>0.0200</td>
-      <td>0.1300</td>
-      <td>0.7700</td>
-      <td>0.0210</td>
-      <td>0.1940</td>
-      <td>0.0000</td>
-      <td>0.7850</td>
-      <td>0.0945</td>
-      <td>2.1586</td>
-      <td>2.1586</td>
-      <td>0.4788</td>
-      <td>0.2349</td>
-      <td>0.4839</td>
-      <td>0.8218</td>
-    </tr>
-    <tr>
-      <th>18</th>
-      <td>0.1000</td>
-      <td>0.9000</td>
-      <td>0.0800</td>
-      <td>0.0200</td>
-      <td>0.1200</td>
-      <td>0.7800</td>
-      <td>0.0178</td>
-      <td>0.1358</td>
-      <td>0.0000</td>
-      <td>0.8464</td>
-      <td>0.0911</td>
-      <td>1.6024</td>
-      <td>1.6024</td>
-      <td>0.4074</td>
-      <td>0.2009</td>
-      <td>0.4667</td>
-      <td>0.7928</td>
-    </tr>
-    <tr>
-      <th>19</th>
-      <td>0.1000</td>
-      <td>0.9000</td>
-      <td>0.0700</td>
-      <td>0.0300</td>
-      <td>0.1000</td>
-      <td>0.8000</td>
-      <td>0.0352</td>
-      <td>0.1377</td>
-      <td>0.0000</td>
-      <td>0.8271</td>
-      <td>0.0674</td>
-      <td>1.1316</td>
-      <td>1.1316</td>
-      <td>0.2906</td>
-      <td>0.1443</td>
-      <td>0.4815</td>
-      <td>0.6616</td>
-    </tr>
-    <tr>
-      <th>20</th>
-      <td>0.2000</td>
-      <td>0.8000</td>
-      <td>0.2000</td>
-      <td>0.0000</td>
-      <td>0.1100</td>
-      <td>0.6900</td>
-      <td>0.1176</td>
-      <td>0.2243</td>
-      <td>0.0000</td>
-      <td>0.6581</td>
-      <td>0.1122</td>
-      <td>11.5671</td>
-      <td>11.5671</td>
-      <td>0.4795</td>
-      <td>0.2353</td>
-      <td>0.2157</td>
-      <td>0.4881</td>
-    </tr>
-    <tr>
-      <th>21</th>
-      <td>0.2000</td>
-      <td>0.8000</td>
-      <td>0.2000</td>
-      <td>0.0000</td>
-      <td>0.0600</td>
-      <td>0.7400</td>
-      <td>0.1503</td>
-      <td>0.0440</td>
-      <td>0.0003</td>
-      <td>0.8054</td>
-      <td>0.0547</td>
-      <td>2.5108</td>
-      <td>2.5108</td>
-      <td>0.1455</td>
-      <td>0.0726</td>
-      <td>0.1304</td>
-      <td>0.1284</td>
-    </tr>
-    <tr>
-      <th>22</th>
-      <td>0.2000</td>
-      <td>0.8000</td>
-      <td>0.1800</td>
-      <td>0.0200</td>
-      <td>0.0700</td>
-      <td>0.7300</td>
-      <td>0.0650</td>
-      <td>0.1182</td>
-      <td>0.0000</td>
-      <td>0.8168</td>
-      <td>0.0925</td>
-      <td>1.4004</td>
-      <td>1.4004</td>
-      <td>0.2610</td>
-      <td>0.1297</td>
-      <td>0.2000</td>
-      <td>0.4762</td>
-    </tr>
-    <tr>
-      <th>23</th>
-      <td>0.2000</td>
-      <td>0.8000</td>
-      <td>0.1900</td>
-      <td>0.0100</td>
-      <td>0.1100</td>
-      <td>0.6900</td>
-      <td>0.1518</td>
-      <td>0.0209</td>
-      <td>0.0000</td>
-      <td>0.8273</td>
-      <td>0.0741</td>
-      <td>0.5186</td>
-      <td>0.5186</td>
-      <td>0.2696</td>
-      <td>0.1340</td>
-      <td>0.2400</td>
-      <td>0.0643</td>
-    </tr>
-    <tr>
-      <th>24</th>
-      <td>0.2000</td>
-      <td>0.8000</td>
-      <td>0.1900</td>
-      <td>0.0100</td>
-      <td>0.1100</td>
-      <td>0.6900</td>
-      <td>0.0676</td>
-      <td>0.3840</td>
-      <td>0.0000</td>
-      <td>0.5484</td>
-      <td>0.1870</td>
-      <td>6.6805</td>
-      <td>6.6805</td>
-      <td>0.6627</td>
-      <td>0.3197</td>
-      <td>0.2400</td>
-      <td>0.7395</td>
-    </tr>
-    <tr>
-      <th>25</th>
-      <td>0.2000</td>
-      <td>0.8000</td>
-      <td>0.2000</td>
-      <td>0.0000</td>
-      <td>0.0800</td>
-      <td>0.7200</td>
-      <td>0.1245</td>
-      <td>0.1006</td>
-      <td>0.0000</td>
-      <td>0.7749</td>
-      <td>0.0778</td>
-      <td>5.3763</td>
-      <td>5.3763</td>
-      <td>0.2668</td>
-      <td>0.1326</td>
-      <td>0.1667</td>
-      <td>0.2878</td>
-    </tr>
-    <tr>
-      <th>26</th>
-      <td>0.2000</td>
-      <td>0.8000</td>
-      <td>0.1900</td>
-      <td>0.0100</td>
-      <td>0.1000</td>
-      <td>0.7000</td>
-      <td>0.1407</td>
-      <td>0.0986</td>
-      <td>0.0000</td>
-      <td>0.7608</td>
-      <td>0.0747</td>
-      <td>1.7989</td>
-      <td>1.7989</td>
-      <td>0.2893</td>
-      <td>0.1436</td>
-      <td>0.2245</td>
-      <td>0.2594</td>
-    </tr>
-    <tr>
-      <th>27</th>
-      <td>0.2000</td>
-      <td>0.8000</td>
-      <td>0.1900</td>
-      <td>0.0100</td>
-      <td>0.0900</td>
-      <td>0.7100</td>
-      <td>0.0992</td>
-      <td>0.1236</td>
-      <td>0.0000</td>
-      <td>0.7772</td>
-      <td>0.0904</td>
-      <td>2.2699</td>
-      <td>2.2699</td>
-      <td>0.3055</td>
-      <td>0.1516</td>
-      <td>0.2083</td>
-      <td>0.3838</td>
-    </tr>
-    <tr>
-      <th>28</th>
-      <td>0.2000</td>
-      <td>0.8000</td>
-      <td>0.2000</td>
-      <td>0.0000</td>
-      <td>0.0700</td>
-      <td>0.7300</td>
-      <td>0.1562</td>
-      <td>0.0755</td>
-      <td>0.0000</td>
-      <td>0.7683</td>
-      <td>0.0569</td>
-      <td>4.0727</td>
-      <td>4.0727</td>
-      <td>0.2011</td>
-      <td>0.1002</td>
-      <td>0.1489</td>
-      <td>0.1945</td>
-    </tr>
-    <tr>
-      <th>29</th>
-      <td>0.2000</td>
-      <td>0.8000</td>
-      <td>0.1900</td>
-      <td>0.0100</td>
-      <td>0.1000</td>
-      <td>0.7000</td>
-      <td>0.1202</td>
-      <td>0.0460</td>
-      <td>0.0000</td>
-      <td>0.8338</td>
-      <td>0.0849</td>
-      <td>0.9743</td>
-      <td>0.9743</td>
-      <td>0.2652</td>
-      <td>0.1318</td>
-      <td>0.2245</td>
-      <td>0.1605</td>
-    </tr>
-    <tr>
-      <th>30</th>
-      <td>0.3000</td>
-      <td>0.7000</td>
-      <td>0.2900</td>
-      <td>0.0100</td>
-      <td>0.0800</td>
-      <td>0.6200</td>
-      <td>0.2028</td>
-      <td>0.1660</td>
-      <td>0.0000</td>
-      <td>0.6313</td>
-      <td>0.0836</td>
-      <td>2.9131</td>
-      <td>2.9131</td>
-      <td>0.2966</td>
-      <td>0.1472</td>
-      <td>0.1343</td>
-      <td>0.2904</td>
-    </tr>
-    <tr>
-      <th>31</th>
-      <td>0.3000</td>
-      <td>0.7000</td>
-      <td>0.2900</td>
-      <td>0.0100</td>
-      <td>0.0500</td>
-      <td>0.6500</td>
-      <td>0.2359</td>
-      <td>0.1008</td>
-      <td>0.0000</td>
-      <td>0.6634</td>
-      <td>0.0521</td>
-      <td>1.7908</td>
-      <td>1.7908</td>
-      <td>0.1491</td>
-      <td>0.0744</td>
-      <td>0.0938</td>
-      <td>0.1760</td>
-    </tr>
-    <tr>
-      <th>32</th>
-      <td>0.3000</td>
-      <td>0.7000</td>
-      <td>0.2700</td>
-      <td>0.0300</td>
-      <td>0.0700</td>
-      <td>0.6300</td>
-      <td>0.1881</td>
-      <td>0.2181</td>
-      <td>0.0000</td>
-      <td>0.5938</td>
-      <td>0.0940</td>
-      <td>1.6654</td>
-      <td>1.6654</td>
-      <td>0.2728</td>
-      <td>0.1355</td>
-      <td>0.1562</td>
-      <td>0.3670</td>
-    </tr>
-    <tr>
-      <th>33</th>
-      <td>0.3000</td>
-      <td>0.7000</td>
-      <td>0.2800</td>
-      <td>0.0200</td>
-      <td>0.0600</td>
-      <td>0.6400</td>
-      <td>0.2094</td>
-      <td>0.0879</td>
-      <td>0.0000</td>
-      <td>0.7028</td>
-      <td>0.0653</td>
-      <td>0.9956</td>
-      <td>0.9956</td>
-      <td>0.1552</td>
-      <td>0.0774</td>
-      <td>0.1250</td>
-      <td>0.1734</td>
-    </tr>
-    <tr>
-      <th>34</th>
-      <td>0.3000</td>
-      <td>0.7000</td>
-      <td>0.2800</td>
-      <td>0.0200</td>
-      <td>0.0300</td>
-      <td>0.6700</td>
-      <td>0.1789</td>
-      <td>0.0805</td>
-      <td>0.0000</td>
-      <td>0.7407</td>
-      <td>0.0656</td>
-      <td>0.9339</td>
-      <td>0.9339</td>
-      <td>0.0951</td>
-      <td>0.0475</td>
-      <td>0.0820</td>
-      <td>0.1836</td>
-    </tr>
-    <tr>
-      <th>35</th>
-      <td>0.3000</td>
-      <td>0.7000</td>
-      <td>0.2900</td>
-      <td>0.0100</td>
-      <td>0.1000</td>
-      <td>0.6000</td>
-      <td>0.1698</td>
-      <td>0.2419</td>
-      <td>0.0000</td>
-      <td>0.5884</td>
-      <td>0.1159</td>
-      <td>4.2090</td>
-      <td>4.2090</td>
-      <td>0.4438</td>
-      <td>0.2183</td>
-      <td>0.1594</td>
-      <td>0.4160</td>
-    </tr>
-    <tr>
-      <th>36</th>
-      <td>0.3000</td>
-      <td>0.7000</td>
-      <td>0.3000</td>
-      <td>0.0000</td>
-      <td>0.0800</td>
-      <td>0.6200</td>
-      <td>0.2506</td>
-      <td>0.0692</td>
-      <td>0.0000</td>
-      <td>0.6801</td>
-      <td>0.0647</td>
-      <td>3.7608</td>
-      <td>3.7608</td>
-      <td>0.2238</td>
-      <td>0.1114</td>
-      <td>0.1176</td>
-      <td>0.1213</td>
-    </tr>
-    <tr>
-      <th>37</th>
-      <td>0.3000</td>
-      <td>0.7000</td>
-      <td>0.2400</td>
-      <td>0.0600</td>
-      <td>0.0700</td>
-      <td>0.6300</td>
-      <td>0.0637</td>
-      <td>0.3139</td>
-      <td>0.0000</td>
-      <td>0.6225</td>
-      <td>0.1269</td>
-      <td>1.3927</td>
-      <td>1.3927</td>
-      <td>0.4190</td>
-      <td>0.2065</td>
-      <td>0.2131</td>
-      <td>0.7114</td>
-    </tr>
-    <tr>
-      <th>38</th>
-      <td>0.3000</td>
-      <td>0.7000</td>
-      <td>0.2700</td>
-      <td>0.0300</td>
-      <td>0.0600</td>
-      <td>0.6400</td>
-      <td>0.1499</td>
-      <td>0.2303</td>
-      <td>0.0000</td>
-      <td>0.6198</td>
-      <td>0.1001</td>
-      <td>1.7782</td>
-      <td>1.7782</td>
-      <td>0.2783</td>
-      <td>0.1383</td>
-      <td>0.1429</td>
-      <td>0.4343</td>
-    </tr>
-    <tr>
-      <th>39</th>
-      <td>0.3000</td>
-      <td>0.7000</td>
-      <td>0.2800</td>
-      <td>0.0200</td>
-      <td>0.0400</td>
-      <td>0.6600</td>
-      <td>0.1516</td>
-      <td>0.1818</td>
-      <td>0.0000</td>
-      <td>0.6666</td>
-      <td>0.0842</td>
-      <td>1.9554</td>
-      <td>1.9554</td>
-      <td>0.2127</td>
-      <td>0.1060</td>
-      <td>0.0968</td>
-      <td>0.3749</td>
-    </tr>
-    <tr>
-      <th>40</th>
-      <td>0.4000</td>
-      <td>0.6000</td>
-      <td>0.3900</td>
-      <td>0.0100</td>
-      <td>0.0700</td>
-      <td>0.5300</td>
-      <td>0.2261</td>
-      <td>0.3143</td>
-      <td>0.0000</td>
-      <td>0.4595</td>
-      <td>0.1522</td>
-      <td>5.4423</td>
-      <td>5.4423</td>
-      <td>0.4445</td>
-      <td>0.2187</td>
-      <td>0.0930</td>
-      <td>0.4100</td>
-    </tr>
-    <tr>
-      <th>41</th>
-      <td>0.4000</td>
-      <td>0.6000</td>
-      <td>0.3500</td>
-      <td>0.0500</td>
-      <td>0.0500</td>
-      <td>0.5500</td>
-      <td>0.2613</td>
-      <td>0.1893</td>
-      <td>0.0000</td>
-      <td>0.5494</td>
-      <td>0.0697</td>
-      <td>0.9233</td>
-      <td>0.9233</td>
-      <td>0.1652</td>
-      <td>0.0824</td>
-      <td>0.1250</td>
-      <td>0.2660</td>
-    </tr>
-    <tr>
-      <th>42</th>
-      <td>0.4000</td>
-      <td>0.6000</td>
-      <td>0.3800</td>
-      <td>0.0200</td>
-      <td>0.0800</td>
-      <td>0.5200</td>
-      <td>0.2133</td>
-      <td>0.2490</td>
-      <td>0.0000</td>
-      <td>0.5377</td>
-      <td>0.1234</td>
-      <td>2.6422</td>
-      <td>2.6422</td>
-      <td>0.3839</td>
-      <td>0.1896</td>
-      <td>0.1163</td>
-      <td>0.3686</td>
-    </tr>
-    <tr>
-      <th>43</th>
-      <td>0.4000</td>
-      <td>0.6000</td>
-      <td>0.3900</td>
-      <td>0.0100</td>
-      <td>0.0500</td>
-      <td>0.5500</td>
-      <td>0.2247</td>
-      <td>0.3492</td>
-      <td>0.0000</td>
-      <td>0.4261</td>
-      <td>0.1696</td>
-      <td>6.0405</td>
-      <td>6.0405</td>
-      <td>0.4388</td>
-      <td>0.2159</td>
-      <td>0.0714</td>
-      <td>0.4373</td>
-    </tr>
-    <tr>
-      <th>44</th>
-      <td>0.4000</td>
-      <td>0.6000</td>
-      <td>0.3800</td>
-      <td>0.0200</td>
-      <td>0.0900</td>
-      <td>0.5100</td>
-      <td>0.2634</td>
-      <td>0.2214</td>
-      <td>0.0000</td>
-      <td>0.5152</td>
-      <td>0.1033</td>
-      <td>2.3290</td>
-      <td>2.3290</td>
-      <td>0.3583</td>
-      <td>0.1773</td>
-      <td>0.1264</td>
-      <td>0.2959</td>
-    </tr>
-    <tr>
-      <th>45</th>
-      <td>0.4000</td>
-      <td>0.6000</td>
-      <td>0.3800</td>
-      <td>0.0200</td>
-      <td>0.0600</td>
-      <td>0.5400</td>
-      <td>0.2125</td>
-      <td>0.2861</td>
-      <td>0.0000</td>
-      <td>0.5014</td>
-      <td>0.1331</td>
-      <td>3.0184</td>
-      <td>3.0184</td>
-      <td>0.3653</td>
-      <td>0.1806</td>
-      <td>0.0952</td>
-      <td>0.4024</td>
-    </tr>
-    <tr>
-      <th>46</th>
-      <td>0.4000</td>
-      <td>0.6000</td>
-      <td>0.3900</td>
-      <td>0.0100</td>
-      <td>0.0500</td>
-      <td>0.5500</td>
-      <td>0.3225</td>
-      <td>0.1608</td>
-      <td>0.0000</td>
-      <td>0.5166</td>
-      <td>0.0754</td>
-      <td>2.7990</td>
-      <td>2.7990</td>
-      <td>0.2042</td>
-      <td>0.1018</td>
-      <td>0.0714</td>
-      <td>0.1996</td>
-    </tr>
-    <tr>
-      <th>47</th>
-      <td>0.4000</td>
-      <td>0.6000</td>
-      <td>0.3600</td>
-      <td>0.0400</td>
-      <td>0.0500</td>
-      <td>0.5500</td>
-      <td>0.1931</td>
-      <td>0.3664</td>
-      <td>0.0000</td>
-      <td>0.4405</td>
-      <td>0.1632</td>
-      <td>2.2041</td>
-      <td>2.2041</td>
-      <td>0.3819</td>
-      <td>0.1887</td>
-      <td>0.1111</td>
-      <td>0.4868</td>
-    </tr>
-    <tr>
-      <th>48</th>
-      <td>0.4000</td>
-      <td>0.6000</td>
-      <td>0.3500</td>
-      <td>0.0500</td>
-      <td>0.0400</td>
-      <td>0.5600</td>
-      <td>0.2412</td>
-      <td>0.1834</td>
-      <td>0.0000</td>
-      <td>0.5754</td>
-      <td>0.0744</td>
-      <td>0.9119</td>
-      <td>0.9119</td>
-      <td>0.1458</td>
-      <td>0.0728</td>
-      <td>0.1139</td>
-      <td>0.2754</td>
-    </tr>
-    <tr>
-      <th>49</th>
-      <td>0.4000</td>
-      <td>0.6000</td>
-      <td>0.3800</td>
-      <td>0.0200</td>
-      <td>0.0800</td>
-      <td>0.5200</td>
-      <td>0.2901</td>
-      <td>0.1835</td>
-      <td>0.0000</td>
-      <td>0.5264</td>
-      <td>0.0849</td>
-      <td>1.9318</td>
-      <td>1.9318</td>
-      <td>0.2863</td>
-      <td>0.1422</td>
-      <td>0.1163</td>
-      <td>0.2403</td>
-    </tr>
-    <tr>
-      <th>50</th>
-      <td>0.5000</td>
-      <td>0.5000</td>
-      <td>0.4700</td>
-      <td>0.0300</td>
-      <td>0.0200</td>
-      <td>0.4800</td>
-      <td>0.2661</td>
-      <td>0.2548</td>
-      <td>0.0000</td>
-      <td>0.4791</td>
-      <td>0.1124</td>
-      <td>1.9135</td>
-      <td>1.9135</td>
-      <td>0.2373</td>
-      <td>0.1181</td>
-      <td>0.0505</td>
-      <td>0.3237</td>
-    </tr>
-    <tr>
-      <th>51</th>
-      <td>0.5000</td>
-      <td>0.5000</td>
-      <td>0.4600</td>
-      <td>0.0400</td>
-      <td>0.0700</td>
-      <td>0.4300</td>
-      <td>0.3066</td>
-      <td>0.2872</td>
-      <td>0.0000</td>
-      <td>0.4062</td>
-      <td>0.1236</td>
-      <td>1.7029</td>
-      <td>1.7029</td>
-      <td>0.3295</td>
-      <td>0.1633</td>
-      <td>0.1068</td>
-      <td>0.3190</td>
-    </tr>
-    <tr>
-      <th>52</th>
-      <td>0.5000</td>
-      <td>0.5000</td>
-      <td>0.4900</td>
-      <td>0.0100</td>
-      <td>0.0700</td>
-      <td>0.4300</td>
-      <td>0.4488</td>
-      <td>0.0499</td>
-      <td>0.0000</td>
-      <td>0.5013</td>
-      <td>0.0556</td>
-      <td>0.9601</td>
-      <td>0.9601</td>
-      <td>0.1606</td>
-      <td>0.0801</td>
-      <td>0.0755</td>
-      <td>0.0527</td>
-    </tr>
-    <tr>
-      <th>53</th>
-      <td>0.5000</td>
-      <td>0.5000</td>
-      <td>0.4700</td>
-      <td>0.0300</td>
-      <td>0.0100</td>
-      <td>0.4900</td>
-      <td>0.3117</td>
-      <td>0.2813</td>
-      <td>0.0000</td>
-      <td>0.4070</td>
-      <td>0.1257</td>
-      <td>2.0871</td>
-      <td>2.0871</td>
-      <td>0.2263</td>
-      <td>0.1127</td>
-      <td>0.0408</td>
-      <td>0.3109</td>
-    </tr>
-    <tr>
-      <th>54</th>
-      <td>0.5000</td>
-      <td>0.5000</td>
-      <td>0.4700</td>
-      <td>0.0300</td>
-      <td>0.0500</td>
-      <td>0.4500</td>
-      <td>0.3857</td>
-      <td>0.1325</td>
-      <td>0.0000</td>
-      <td>0.4818</td>
-      <td>0.0672</td>
-      <td>1.0215</td>
-      <td>1.0215</td>
-      <td>0.1461</td>
-      <td>0.0729</td>
-      <td>0.0784</td>
-      <td>0.1466</td>
-    </tr>
-    <tr>
-      <th>55</th>
-      <td>0.5000</td>
-      <td>0.5000</td>
-      <td>0.4500</td>
-      <td>0.0500</td>
-      <td>0.0900</td>
-      <td>0.4100</td>
-      <td>0.3611</td>
-      <td>0.2164</td>
-      <td>0.0000</td>
-      <td>0.4224</td>
-      <td>0.0894</td>
-      <td>1.0498</td>
-      <td>1.0498</td>
-      <td>0.2897</td>
-      <td>0.1438</td>
-      <td>0.1346</td>
-      <td>0.2306</td>
-    </tr>
-    <tr>
-      <th>56</th>
-      <td>0.5000</td>
-      <td>0.5000</td>
-      <td>0.4300</td>
-      <td>0.0700</td>
-      <td>0.0700</td>
-      <td>0.4300</td>
-      <td>0.2665</td>
-      <td>0.3518</td>
-      <td>0.0000</td>
-      <td>0.3817</td>
-      <td>0.1409</td>
-      <td>1.2942</td>
-      <td>1.2942</td>
-      <td>0.3423</td>
-      <td>0.1695</td>
-      <td>0.1400</td>
-      <td>0.3975</td>
-    </tr>
-    <tr>
-      <th>57</th>
-      <td>0.5000</td>
-      <td>0.5000</td>
-      <td>0.4600</td>
-      <td>0.0400</td>
-      <td>0.0300</td>
-      <td>0.4700</td>
-      <td>0.2969</td>
-      <td>0.2003</td>
-      <td>0.0000</td>
-      <td>0.5028</td>
-      <td>0.0966</td>
-      <td>1.2099</td>
-      <td>1.2099</td>
-      <td>0.1690</td>
-      <td>0.0843</td>
-      <td>0.0707</td>
-      <td>0.2523</td>
-    </tr>
-    <tr>
-      <th>58</th>
-      <td>0.5000</td>
-      <td>0.5000</td>
-      <td>0.4900</td>
-      <td>0.0100</td>
-      <td>0.0200</td>
-      <td>0.4800</td>
-      <td>0.4031</td>
-      <td>0.0720</td>
-      <td>0.0000</td>
-      <td>0.5249</td>
-      <td>0.0534</td>
-      <td>1.3003</td>
-      <td>1.3003</td>
-      <td>0.0683</td>
-      <td>0.0341</td>
-      <td>0.0297</td>
-      <td>0.0820</td>
-    </tr>
-    <tr>
-      <th>59</th>
-      <td>0.5000</td>
-      <td>0.5000</td>
-      <td>0.4900</td>
-      <td>0.0100</td>
-      <td>0.0600</td>
-      <td>0.4400</td>
-      <td>0.3352</td>
-      <td>0.3022</td>
-      <td>0.0000</td>
-      <td>0.3626</td>
-      <td>0.1461</td>
-      <td>5.2225</td>
-      <td>5.2225</td>
-      <td>0.3921</td>
-      <td>0.1936</td>
-      <td>0.0667</td>
-      <td>0.3107</td>
-    </tr>
-    <tr>
-      <th>60</th>
-      <td>0.6000</td>
-      <td>0.4000</td>
-      <td>0.5500</td>
-      <td>0.0500</td>
-      <td>0.0200</td>
-      <td>0.3800</td>
-      <td>0.3723</td>
-      <td>0.2617</td>
-      <td>0.0000</td>
-      <td>0.3660</td>
-      <td>0.1059</td>
-      <td>1.2515</td>
-      <td>1.2515</td>
-      <td>0.1819</td>
-      <td>0.0907</td>
-      <td>0.0598</td>
-      <td>0.2601</td>
-    </tr>
-    <tr>
-      <th>61</th>
-      <td>0.6000</td>
-      <td>0.4000</td>
-      <td>0.5500</td>
-      <td>0.0500</td>
-      <td>0.0600</td>
-      <td>0.3400</td>
-      <td>0.3944</td>
-      <td>0.3478</td>
-      <td>0.0000</td>
-      <td>0.2577</td>
-      <td>0.1489</td>
-      <td>1.7143</td>
-      <td>1.7143</td>
-      <td>0.3410</td>
-      <td>0.1689</td>
-      <td>0.0909</td>
-      <td>0.3060</td>
-    </tr>
-    <tr>
-      <th>62</th>
-      <td>0.6000</td>
-      <td>0.4000</td>
-      <td>0.5800</td>
-      <td>0.0200</td>
-      <td>0.0600</td>
-      <td>0.3400</td>
-      <td>0.4883</td>
-      <td>0.1869</td>
-      <td>0.0000</td>
-      <td>0.3248</td>
-      <td>0.0835</td>
-      <td>1.9503</td>
-      <td>1.9503</td>
-      <td>0.2311</td>
-      <td>0.1150</td>
-      <td>0.0645</td>
-      <td>0.1607</td>
-    </tr>
-    <tr>
-      <th>63</th>
-      <td>0.6000</td>
-      <td>0.4000</td>
-      <td>0.5900</td>
-      <td>0.0100</td>
-      <td>0.0300</td>
-      <td>0.3700</td>
-      <td>0.4691</td>
-      <td>0.3098</td>
-      <td>0.0000</td>
-      <td>0.2211</td>
-      <td>0.1499</td>
-      <td>5.3611</td>
-      <td>5.3611</td>
-      <td>0.3474</td>
-      <td>0.1719</td>
-      <td>0.0328</td>
-      <td>0.2482</td>
-    </tr>
-    <tr>
-      <th>64</th>
-      <td>0.6000</td>
-      <td>0.4000</td>
-      <td>0.5700</td>
-      <td>0.0300</td>
-      <td>0.0600</td>
-      <td>0.3400</td>
-      <td>0.4036</td>
-      <td>0.3264</td>
-      <td>0.0000</td>
-      <td>0.2700</td>
-      <td>0.1482</td>
-      <td>2.4709</td>
-      <td>2.4709</td>
-      <td>0.3627</td>
-      <td>0.1794</td>
-      <td>0.0732</td>
-      <td>0.2879</td>
-    </tr>
-    <tr>
-      <th>65</th>
-      <td>0.6000</td>
-      <td>0.4000</td>
-      <td>0.5700</td>
-      <td>0.0300</td>
-      <td>0.0100</td>
-      <td>0.3900</td>
-      <td>0.3251</td>
-      <td>0.3488</td>
-      <td>0.0000</td>
-      <td>0.3262</td>
-      <td>0.1594</td>
-      <td>2.5904</td>
-      <td>2.5904</td>
-      <td>0.3243</td>
-      <td>0.1607</td>
-      <td>0.0339</td>
-      <td>0.3491</td>
-    </tr>
-    <tr>
-      <th>66</th>
-      <td>0.6000</td>
-      <td>0.4000</td>
-      <td>0.5300</td>
-      <td>0.0700</td>
-      <td>0.0500</td>
-      <td>0.3500</td>
-      <td>0.2977</td>
-      <td>0.4788</td>
-      <td>0.0000</td>
-      <td>0.2235</td>
-      <td>0.2044</td>
-      <td>1.7875</td>
-      <td>1.7875</td>
-      <td>0.4531</td>
-      <td>0.2228</td>
-      <td>0.1017</td>
-      <td>0.4457</td>
-    </tr>
-    <tr>
-      <th>67</th>
-      <td>0.6000</td>
-      <td>0.4000</td>
-      <td>0.5600</td>
-      <td>0.0400</td>
-      <td>0.0600</td>
-      <td>0.3400</td>
-      <td>0.4491</td>
-      <td>0.2367</td>
-      <td>0.0000</td>
-      <td>0.3142</td>
-      <td>0.0983</td>
-      <td>1.3912</td>
-      <td>1.3912</td>
-      <td>0.2413</td>
-      <td>0.1201</td>
-      <td>0.0820</td>
-      <td>0.2086</td>
-    </tr>
-    <tr>
-      <th>68</th>
-      <td>0.6000</td>
-      <td>0.4000</td>
-      <td>0.5600</td>
-      <td>0.0400</td>
-      <td>0.0400</td>
-      <td>0.3600</td>
-      <td>0.4048</td>
-      <td>0.3060</td>
-      <td>0.0000</td>
-      <td>0.2892</td>
-      <td>0.1330</td>
-      <td>1.8169</td>
-      <td>1.8169</td>
-      <td>0.2720</td>
-      <td>0.1352</td>
-      <td>0.0667</td>
-      <td>0.2743</td>
-    </tr>
-    <tr>
-      <th>69</th>
-      <td>0.6000</td>
-      <td>0.4000</td>
-      <td>0.5900</td>
-      <td>0.0100</td>
-      <td>0.0300</td>
-      <td>0.3700</td>
-      <td>0.4462</td>
-      <td>0.2388</td>
-      <td>0.0000</td>
-      <td>0.3150</td>
-      <td>0.1144</td>
-      <td>4.1244</td>
-      <td>4.1244</td>
-      <td>0.2503</td>
-      <td>0.1245</td>
-      <td>0.0328</td>
-      <td>0.2111</td>
-    </tr>
-    <tr>
-      <th>70</th>
-      <td>0.7000</td>
-      <td>0.3000</td>
-      <td>0.6900</td>
-      <td>0.0100</td>
-      <td>0.0200</td>
-      <td>0.2800</td>
-      <td>0.6374</td>
-      <td>0.0534</td>
-      <td>0.0000</td>
-      <td>0.3093</td>
-      <td>0.0363</td>
-      <td>0.9679</td>
-      <td>0.9679</td>
-      <td>0.0468</td>
-      <td>0.0234</td>
-      <td>0.0213</td>
-      <td>0.0402</td>
-    </tr>
-    <tr>
-      <th>71</th>
-      <td>0.7000</td>
-      <td>0.3000</td>
-      <td>0.6600</td>
-      <td>0.0400</td>
-      <td>0.0100</td>
-      <td>0.2900</td>
-      <td>0.5646</td>
-      <td>0.2108</td>
-      <td>0.0000</td>
-      <td>0.2246</td>
-      <td>0.0854</td>
-      <td>1.2071</td>
-      <td>1.2071</td>
-      <td>0.1229</td>
-      <td>0.0614</td>
-      <td>0.0365</td>
-      <td>0.1573</td>
-    </tr>
-    <tr>
-      <th>72</th>
-      <td>0.7000</td>
-      <td>0.3000</td>
-      <td>0.6400</td>
-      <td>0.0600</td>
-      <td>0.0300</td>
-      <td>0.2700</td>
-      <td>0.5626</td>
-      <td>0.1337</td>
-      <td>0.0000</td>
-      <td>0.3037</td>
-      <td>0.0537</td>
-      <td>0.5584</td>
-      <td>0.5584</td>
-      <td>0.0695</td>
-      <td>0.0347</td>
-      <td>0.0657</td>
-      <td>0.1062</td>
-    </tr>
-    <tr>
-      <th>73</th>
-      <td>0.7000</td>
-      <td>0.3000</td>
-      <td>0.6400</td>
-      <td>0.0600</td>
-      <td>0.0100</td>
-      <td>0.2900</td>
-      <td>0.4890</td>
-      <td>0.3280</td>
-      <td>0.0000</td>
-      <td>0.1831</td>
-      <td>0.1340</td>
-      <td>1.3465</td>
-      <td>1.3465</td>
-      <td>0.2152</td>
-      <td>0.1072</td>
-      <td>0.0519</td>
-      <td>0.2512</td>
-    </tr>
-    <tr>
-      <th>74</th>
-      <td>0.7000</td>
-      <td>0.3000</td>
-      <td>0.6300</td>
-      <td>0.0700</td>
-      <td>0.0200</td>
-      <td>0.2800</td>
-      <td>0.4717</td>
-      <td>0.2827</td>
-      <td>0.0000</td>
-      <td>0.2456</td>
-      <td>0.1063</td>
-      <td>1.0015</td>
-      <td>1.0015</td>
-      <td>0.1581</td>
-      <td>0.0789</td>
-      <td>0.0667</td>
-      <td>0.2305</td>
-    </tr>
-    <tr>
-      <th>75</th>
-      <td>0.7000</td>
-      <td>0.3000</td>
-      <td>0.6800</td>
-      <td>0.0200</td>
-      <td>0.0300</td>
-      <td>0.2700</td>
-      <td>0.5914</td>
-      <td>0.2123</td>
-      <td>0.0000</td>
-      <td>0.1963</td>
-      <td>0.0961</td>
-      <td>2.2366</td>
-      <td>2.2366</td>
-      <td>0.1947</td>
-      <td>0.0970</td>
-      <td>0.0355</td>
-      <td>0.1522</td>
-    </tr>
-    <tr>
-      <th>76</th>
-      <td>0.7000</td>
-      <td>0.3000</td>
-      <td>0.6600</td>
-      <td>0.0400</td>
-      <td>0.0300</td>
-      <td>0.2700</td>
-      <td>0.6164</td>
-      <td>0.1300</td>
-      <td>0.0000</td>
-      <td>0.2535</td>
-      <td>0.0450</td>
-      <td>0.7457</td>
-      <td>0.7457</td>
-      <td>0.0807</td>
-      <td>0.0403</td>
-      <td>0.0504</td>
-      <td>0.0954</td>
-    </tr>
-    <tr>
-      <th>77</th>
-      <td>0.7000</td>
-      <td>0.3000</td>
-      <td>0.6600</td>
-      <td>0.0400</td>
-      <td>0.0100</td>
-      <td>0.2900</td>
-      <td>0.5485</td>
-      <td>0.1940</td>
-      <td>0.0000</td>
-      <td>0.2575</td>
-      <td>0.0770</td>
-      <td>1.0917</td>
-      <td>1.0917</td>
-      <td>0.1061</td>
-      <td>0.0530</td>
-      <td>0.0365</td>
-      <td>0.1503</td>
-    </tr>
-    <tr>
-      <th>78</th>
-      <td>0.7000</td>
-      <td>0.3000</td>
-      <td>0.6600</td>
-      <td>0.0400</td>
-      <td>0.0500</td>
-      <td>0.2500</td>
-      <td>0.5583</td>
-      <td>0.2694</td>
-      <td>0.0000</td>
-      <td>0.1723</td>
-      <td>0.1147</td>
-      <td>1.6161</td>
-      <td>1.6161</td>
-      <td>0.2536</td>
-      <td>0.1261</td>
-      <td>0.0638</td>
-      <td>0.1944</td>
-    </tr>
-    <tr>
-      <th>79</th>
-      <td>0.7000</td>
-      <td>0.3000</td>
-      <td>0.6800</td>
-      <td>0.0200</td>
-      <td>0.0300</td>
-      <td>0.2700</td>
-      <td>0.4831</td>
-      <td>0.2938</td>
-      <td>0.0000</td>
-      <td>0.2231</td>
-      <td>0.1369</td>
-      <td>3.0667</td>
-      <td>3.0667</td>
-      <td>0.2896</td>
-      <td>0.1438</td>
-      <td>0.0355</td>
-      <td>0.2332</td>
-    </tr>
-    <tr>
-      <th>80</th>
-      <td>0.8000</td>
-      <td>0.2000</td>
-      <td>0.7300</td>
-      <td>0.0700</td>
-      <td>0.0300</td>
-      <td>0.1700</td>
-      <td>0.5847</td>
-      <td>0.3097</td>
-      <td>0.0000</td>
-      <td>0.1056</td>
-      <td>0.1199</td>
-      <td>1.1547</td>
-      <td>1.1547</td>
-      <td>0.2027</td>
-      <td>0.1010</td>
-      <td>0.0641</td>
-      <td>0.2094</td>
-    </tr>
-    <tr>
-      <th>81</th>
-      <td>0.8000</td>
-      <td>0.2000</td>
-      <td>0.7300</td>
-      <td>0.0700</td>
-      <td>0.0300</td>
-      <td>0.1700</td>
-      <td>0.5118</td>
-      <td>0.3332</td>
-      <td>0.0000</td>
-      <td>0.1550</td>
-      <td>0.1316</td>
-      <td>1.1873</td>
-      <td>1.1873</td>
-      <td>0.2297</td>
-      <td>0.1144</td>
-      <td>0.0641</td>
-      <td>0.2456</td>
-    </tr>
-    <tr>
-      <th>82</th>
-      <td>0.8000</td>
-      <td>0.2000</td>
-      <td>0.7100</td>
-      <td>0.0900</td>
-      <td>0.0000</td>
-      <td>0.2000</td>
-      <td>0.5372</td>
-      <td>0.2500</td>
-      <td>0.0000</td>
-      <td>0.2127</td>
-      <td>0.0864</td>
-      <td>0.4987</td>
-      <td>0.4987</td>
-      <td>0.0916</td>
-      <td>0.0457</td>
-      <td>0.0596</td>
-      <td>0.1888</td>
-    </tr>
-    <tr>
-      <th>83</th>
-      <td>0.8000</td>
-      <td>0.2000</td>
-      <td>0.7500</td>
-      <td>0.0500</td>
-      <td>0.0700</td>
-      <td>0.1300</td>
-      <td>0.7568</td>
-      <td>0.1519</td>
-      <td>0.0000</td>
-      <td>0.0913</td>
-      <td>0.0544</td>
-      <td>0.7703</td>
-      <td>0.7703</td>
-      <td>0.1843</td>
-      <td>0.0919</td>
-      <td>0.0741</td>
-      <td>0.0912</td>
-    </tr>
-    <tr>
-      <th>84</th>
-      <td>0.8000</td>
-      <td>0.2000</td>
-      <td>0.7600</td>
-      <td>0.0400</td>
-      <td>0.0200</td>
-      <td>0.1800</td>
-      <td>0.7005</td>
-      <td>0.2142</td>
-      <td>0.0000</td>
-      <td>0.0853</td>
-      <td>0.0871</td>
-      <td>1.3152</td>
-      <td>1.3152</td>
-      <td>0.1637</td>
-      <td>0.0816</td>
-      <td>0.0380</td>
-      <td>0.1326</td>
-    </tr>
-    <tr>
-      <th>85</th>
-      <td>0.8000</td>
-      <td>0.2000</td>
-      <td>0.7500</td>
-      <td>0.0500</td>
-      <td>0.0100</td>
-      <td>0.1900</td>
-      <td>0.6569</td>
-      <td>0.1493</td>
-      <td>0.0000</td>
-      <td>0.1938</td>
-      <td>0.0516</td>
-      <td>0.6539</td>
-      <td>0.6539</td>
-      <td>0.0554</td>
-      <td>0.0277</td>
-      <td>0.0385</td>
-      <td>0.1021</td>
-    </tr>
-    <tr>
-      <th>86</th>
-      <td>0.8000</td>
-      <td>0.2000</td>
-      <td>0.7600</td>
-      <td>0.0400</td>
-      <td>0.0100</td>
-      <td>0.1900</td>
-      <td>0.6134</td>
-      <td>0.2222</td>
-      <td>0.0000</td>
-      <td>0.1644</td>
-      <td>0.0911</td>
-      <td>1.2596</td>
-      <td>1.2596</td>
-      <td>0.1338</td>
-      <td>0.0668</td>
-      <td>0.0318</td>
-      <td>0.1534</td>
-    </tr>
-    <tr>
-      <th>87</th>
-      <td>0.8000</td>
-      <td>0.2000</td>
-      <td>0.7500</td>
-      <td>0.0500</td>
-      <td>0.0100</td>
-      <td>0.1900</td>
-      <td>0.6629</td>
-      <td>0.2320</td>
-      <td>0.0000</td>
-      <td>0.1051</td>
-      <td>0.0910</td>
-      <td>1.1313</td>
-      <td>1.1313</td>
-      <td>0.1401</td>
-      <td>0.0699</td>
-      <td>0.0385</td>
-      <td>0.1489</td>
-    </tr>
-    <tr>
-      <th>88</th>
-      <td>0.8000</td>
-      <td>0.2000</td>
-      <td>0.7400</td>
-      <td>0.0600</td>
-      <td>0.0000</td>
-      <td>0.2000</td>
-      <td>0.5857</td>
-      <td>0.3081</td>
-      <td>0.0000</td>
-      <td>0.1062</td>
-      <td>0.1240</td>
-      <td>1.1204</td>
-      <td>1.1204</td>
-      <td>0.1961</td>
-      <td>0.0977</td>
-      <td>0.0390</td>
-      <td>0.2082</td>
-    </tr>
-    <tr>
-      <th>89</th>
-      <td>0.8000</td>
-      <td>0.2000</td>
-      <td>0.7400</td>
-      <td>0.0600</td>
-      <td>0.0200</td>
-      <td>0.1800</td>
-      <td>0.4581</td>
-      <td>0.4695</td>
-      <td>0.0000</td>
-      <td>0.0724</td>
-      <td>0.2048</td>
-      <td>2.0152</td>
-      <td>2.0152</td>
-      <td>0.4265</td>
-      <td>0.2101</td>
-      <td>0.0513</td>
-      <td>0.3389</td>
-    </tr>
-    <tr>
-      <th>90</th>
-      <td>0.9000</td>
-      <td>0.1000</td>
-      <td>0.8600</td>
-      <td>0.0400</td>
-      <td>0.0200</td>
-      <td>0.0800</td>
-      <td>0.8681</td>
-      <td>0.1034</td>
-      <td>0.0000</td>
-      <td>0.0284</td>
-      <td>0.0358</td>
-      <td>0.7063</td>
-      <td>0.7063</td>
-      <td>0.0718</td>
-      <td>0.0359</td>
-      <td>0.0337</td>
-      <td>0.0562</td>
-    </tr>
-    <tr>
-      <th>91</th>
-      <td>0.9000</td>
-      <td>0.1000</td>
-      <td>0.8200</td>
-      <td>0.0800</td>
-      <td>0.0200</td>
-      <td>0.0800</td>
-      <td>0.6954</td>
-      <td>0.2312</td>
-      <td>0.0002</td>
-      <td>0.0732</td>
-      <td>0.0756</td>
-      <td>0.7002</td>
-      <td>0.7002</td>
-      <td>0.0945</td>
-      <td>0.0472</td>
-      <td>0.0575</td>
-      <td>0.1426</td>
-    </tr>
-    <tr>
-      <th>92</th>
-      <td>0.9000</td>
-      <td>0.1000</td>
-      <td>0.8500</td>
-      <td>0.0500</td>
-      <td>0.0100</td>
-      <td>0.0900</td>
-      <td>0.7136</td>
-      <td>0.2209</td>
-      <td>0.0001</td>
-      <td>0.0654</td>
-      <td>0.0854</td>
-      <td>1.0458</td>
-      <td>1.0458</td>
-      <td>0.1155</td>
-      <td>0.0577</td>
-      <td>0.0341</td>
-      <td>0.1341</td>
-    </tr>
-    <tr>
-      <th>93</th>
-      <td>0.9000</td>
-      <td>0.1000</td>
-      <td>0.8300</td>
-      <td>0.0700</td>
-      <td>0.0100</td>
-      <td>0.0900</td>
-      <td>0.6537</td>
-      <td>0.2912</td>
-      <td>0.0000</td>
-      <td>0.0552</td>
-      <td>0.1106</td>
-      <td>1.0483</td>
-      <td>1.0483</td>
-      <td>0.1549</td>
-      <td>0.0773</td>
-      <td>0.0460</td>
-      <td>0.1822</td>
-    </tr>
-    <tr>
-      <th>94</th>
-      <td>0.9000</td>
-      <td>0.1000</td>
-      <td>0.8300</td>
-      <td>0.0700</td>
-      <td>0.0000</td>
-      <td>0.1000</td>
-      <td>0.8285</td>
-      <td>0.0810</td>
-      <td>0.0000</td>
-      <td>0.0905</td>
-      <td>0.0055</td>
-      <td>0.0602</td>
-      <td>0.0602</td>
-      <td>0.0012</td>
-      <td>0.0006</td>
-      <td>0.0405</td>
-      <td>0.0466</td>
-    </tr>
-    <tr>
-      <th>95</th>
-      <td>0.9000</td>
-      <td>0.1000</td>
-      <td>0.8500</td>
-      <td>0.0500</td>
-      <td>0.0000</td>
-      <td>0.1000</td>
-      <td>0.7245</td>
-      <td>0.2244</td>
-      <td>0.0000</td>
-      <td>0.0511</td>
-      <td>0.0872</td>
-      <td>0.9459</td>
-      <td>0.9459</td>
-      <td>0.1230</td>
-      <td>0.0614</td>
-      <td>0.0286</td>
-      <td>0.1341</td>
-    </tr>
-    <tr>
-      <th>96</th>
-      <td>0.9000</td>
-      <td>0.1000</td>
-      <td>0.8600</td>
-      <td>0.0400</td>
-      <td>0.0100</td>
-      <td>0.0900</td>
-      <td>0.6797</td>
-      <td>0.3202</td>
-      <td>0.0000</td>
-      <td>0.0001</td>
-      <td>0.1401</td>
-      <td>2.0120</td>
-      <td>2.0120</td>
-      <td>0.4073</td>
-      <td>0.2009</td>
-      <td>0.0282</td>
-      <td>0.1907</td>
-    </tr>
-    <tr>
-      <th>97</th>
-      <td>0.9000</td>
-      <td>0.1000</td>
-      <td>0.8500</td>
-      <td>0.0500</td>
-      <td>0.0000</td>
-      <td>0.1000</td>
-      <td>0.7974</td>
-      <td>0.1166</td>
-      <td>0.0000</td>
-      <td>0.0859</td>
-      <td>0.0333</td>
-      <td>0.3517</td>
-      <td>0.3517</td>
-      <td>0.0257</td>
-      <td>0.0128</td>
-      <td>0.0286</td>
-      <td>0.0681</td>
-    </tr>
-    <tr>
-      <th>98</th>
-      <td>0.9000</td>
-      <td>0.1000</td>
-      <td>0.8600</td>
-      <td>0.0400</td>
-      <td>0.0100</td>
-      <td>0.0900</td>
-      <td>0.7810</td>
-      <td>0.2040</td>
-      <td>0.0001</td>
-      <td>0.0149</td>
-      <td>0.0820</td>
-      <td>1.2967</td>
-      <td>1.2967</td>
-      <td>0.1783</td>
-      <td>0.0889</td>
-      <td>0.0282</td>
-      <td>0.1155</td>
-    </tr>
-    <tr>
-      <th>99</th>
-      <td>0.9000</td>
-      <td>0.1000</td>
-      <td>0.8400</td>
-      <td>0.0600</td>
-      <td>0.0000</td>
-      <td>0.1000</td>
-      <td>0.7152</td>
-      <td>0.1607</td>
-      <td>0.0000</td>
-      <td>0.1242</td>
-      <td>0.0624</td>
-      <td>0.4817</td>
-      <td>0.4817</td>
-      <td>0.0525</td>
-      <td>0.0263</td>
-      <td>0.0345</td>
-      <td>0.1010</td>
-    </tr>
-    <tr>
-      <th>100</th>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.9300</td>
-      <td>0.0700</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.9565</td>
-      <td>0.0229</td>
-      <td>0.0000</td>
-      <td>0.0206</td>
-      <td>0.0236</td>
-      <td>1.1924</td>
-      <td>1.1924</td>
-      <td>0.0399</td>
-      <td>0.0199</td>
-      <td>0.0363</td>
-      <td>0.0118</td>
-    </tr>
-    <tr>
-      <th>101</th>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.9400</td>
-      <td>0.0600</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.8954</td>
-      <td>0.0736</td>
-      <td>0.0000</td>
-      <td>0.0310</td>
-      <td>0.0223</td>
-      <td>1.6128</td>
-      <td>1.6128</td>
-      <td>0.0234</td>
-      <td>0.0117</td>
-      <td>0.0309</td>
-      <td>0.0395</td>
-    </tr>
-    <tr>
-      <th>102</th>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.9800</td>
-      <td>0.0200</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.9486</td>
-      <td>0.0514</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.0157</td>
-      <td>0.3220</td>
-      <td>0.3220</td>
-      <td>0.0115</td>
-      <td>0.0058</td>
-      <td>0.0101</td>
-      <td>0.0264</td>
-    </tr>
-    <tr>
-      <th>103</th>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.9400</td>
-      <td>0.0600</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.9317</td>
-      <td>0.0628</td>
-      <td>0.0000</td>
-      <td>0.0054</td>
-      <td>0.0041</td>
-      <td>0.2842</td>
-      <td>0.2842</td>
-      <td>0.0018</td>
-      <td>0.0009</td>
-      <td>0.0309</td>
-      <td>0.0326</td>
-    </tr>
-    <tr>
-      <th>104</th>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.9700</td>
-      <td>0.0300</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.7735</td>
-      <td>0.2265</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.0982</td>
-      <td>1.4540</td>
-      <td>1.4540</td>
-      <td>0.1533</td>
-      <td>0.0765</td>
-      <td>0.0152</td>
-      <td>0.1277</td>
-    </tr>
-    <tr>
-      <th>105</th>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.9300</td>
-      <td>0.0700</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.8494</td>
-      <td>0.1275</td>
-      <td>0.0000</td>
-      <td>0.0231</td>
-      <td>0.0403</td>
-      <td>1.3676</td>
-      <td>1.3676</td>
-      <td>0.0330</td>
-      <td>0.0165</td>
-      <td>0.0363</td>
-      <td>0.0698</td>
-    </tr>
-    <tr>
-      <th>106</th>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.9500</td>
-      <td>0.0500</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.9735</td>
-      <td>0.0148</td>
-      <td>0.0001</td>
-      <td>0.0116</td>
-      <td>0.0176</td>
-      <td>0.7536</td>
-      <td>0.7536</td>
-      <td>0.0269</td>
-      <td>0.0135</td>
-      <td>0.0256</td>
-      <td>0.0076</td>
-    </tr>
-    <tr>
-      <th>107</th>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.9400</td>
-      <td>0.0600</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.9179</td>
-      <td>0.0492</td>
-      <td>0.0000</td>
-      <td>0.0329</td>
-      <td>0.0165</td>
-      <td>1.6944</td>
-      <td>1.6944</td>
-      <td>0.0241</td>
-      <td>0.0120</td>
-      <td>0.0309</td>
-      <td>0.0261</td>
-    </tr>
-    <tr>
-      <th>108</th>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.9600</td>
-      <td>0.0400</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.9019</td>
-      <td>0.0981</td>
-      <td>0.0001</td>
-      <td>0.0000</td>
-      <td>0.0291</td>
-      <td>0.3405</td>
-      <td>0.3405</td>
-      <td>0.0226</td>
-      <td>0.0113</td>
-      <td>0.0204</td>
-      <td>0.0516</td>
-    </tr>
-    <tr>
-      <th>109</th>
-      <td>1.0000</td>
-      <td>0.0000</td>
-      <td>0.9400</td>
-      <td>0.0600</td>
-      <td>0.0000</td>
-      <td>0.0000</td>
-      <td>0.9443</td>
-      <td>0.0236</td>
-      <td>0.0003</td>
-      <td>0.0317</td>
-      <td>0.0182</td>
-      <td>1.7433</td>
-      <td>1.7433</td>
-      <td>0.0387</td>
-      <td>0.0194</td>
-      <td>0.0309</td>
-      <td>0.0125</td>
-    </tr>
-  </tbody>
-</table>
-**************************************************
diff --git a/out_imdb.html b/out_imdb.html
new file mode 100644
index 0000000..aa6dd34
--- /dev/null
+++ b/out_imdb.html
@@ -0,0 +1,801 @@
+<html>
+<head>
+  <style>
+    table, th, td {
+      border: 1px solid black;
+      border-collapse: collapse;
+    }
+    th, td {
+      padding: 4px;
+    }
+    tr:hover{
+      background-color: lightgreen;
+    }
+  </style>
+</head>
+<body>
+<div>imdb</div>
+<div>Tests:</div>
+<div>protocol=APP</div>
+<div>n_prevalences=21</div>
+<div>repreats=1000</div>
+<div>&nbsp;</div>
+<div>binary</div>
+<table class="dataframe">
+  <thead>
+    <tr>
+      <th></th>
+      <th colspan="2" halign="left">base</th>
+      <th colspan="4" halign="left">true</th>
+      <th colspan="4" halign="left">estim</th>
+      <th colspan="4" halign="left">errors</th>
+    </tr>
+    <tr>
+      <th></th>
+      <th>0</th>
+      <th>1</th>
+      <th>T0</th>
+      <th>F1</th>
+      <th>F0</th>
+      <th>T1</th>
+      <th>T0</th>
+      <th>F1</th>
+      <th>F0</th>
+      <th>T1</th>
+      <th>ae</th>
+      <th>f1_true</th>
+      <th>f1_estim</th>
+      <th>f1_dist</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0.0000</td>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.1100</td>
+      <td>0.8900</td>
+      <td>0.0038</td>
+      <td>0.0108</td>
+      <td>0.1062</td>
+      <td>0.8792</td>
+      <td>0.0073</td>
+      <td>NaN</td>
+      <td>0.0565</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>0.0500</td>
+      <td>0.9500</td>
+      <td>0.0448</td>
+      <td>0.0052</td>
+      <td>0.1050</td>
+      <td>0.8450</td>
+      <td>0.0454</td>
+      <td>0.0110</td>
+      <td>0.1044</td>
+      <td>0.8392</td>
+      <td>0.0164</td>
+      <td>0.4570</td>
+      <td>0.4274</td>
+      <td>0.1598</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>0.1000</td>
+      <td>0.9000</td>
+      <td>0.0890</td>
+      <td>0.0110</td>
+      <td>0.0981</td>
+      <td>0.8019</td>
+      <td>0.0928</td>
+      <td>0.0134</td>
+      <td>0.0943</td>
+      <td>0.7995</td>
+      <td>0.0194</td>
+      <td>0.6247</td>
+      <td>0.6280</td>
+      <td>0.1120</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>0.1500</td>
+      <td>0.8500</td>
+      <td>0.1329</td>
+      <td>0.0171</td>
+      <td>0.0948</td>
+      <td>0.7552</td>
+      <td>0.1394</td>
+      <td>0.0157</td>
+      <td>0.0883</td>
+      <td>0.7566</td>
+      <td>0.0245</td>
+      <td>0.7062</td>
+      <td>0.7237</td>
+      <td>0.1044</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>0.2000</td>
+      <td>0.8000</td>
+      <td>0.1776</td>
+      <td>0.0224</td>
+      <td>0.0870</td>
+      <td>0.7130</td>
+      <td>0.1860</td>
+      <td>0.0167</td>
+      <td>0.0785</td>
+      <td>0.7188</td>
+      <td>0.0256</td>
+      <td>0.7661</td>
+      <td>0.7942</td>
+      <td>0.0858</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>0.2500</td>
+      <td>0.7500</td>
+      <td>0.2225</td>
+      <td>0.0275</td>
+      <td>0.0837</td>
+      <td>0.6663</td>
+      <td>0.2324</td>
+      <td>0.0192</td>
+      <td>0.0738</td>
+      <td>0.6746</td>
+      <td>0.0282</td>
+      <td>0.8013</td>
+      <td>0.8316</td>
+      <td>0.0781</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>0.3000</td>
+      <td>0.7000</td>
+      <td>0.2668</td>
+      <td>0.0332</td>
+      <td>0.0760</td>
+      <td>0.6240</td>
+      <td>0.2773</td>
+      <td>0.0219</td>
+      <td>0.0655</td>
+      <td>0.6353</td>
+      <td>0.0293</td>
+      <td>0.8306</td>
+      <td>0.8623</td>
+      <td>0.0677</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>0.3500</td>
+      <td>0.6500</td>
+      <td>0.3100</td>
+      <td>0.0400</td>
+      <td>0.0712</td>
+      <td>0.5788</td>
+      <td>0.3252</td>
+      <td>0.0264</td>
+      <td>0.0560</td>
+      <td>0.5924</td>
+      <td>0.0324</td>
+      <td>0.8480</td>
+      <td>0.8867</td>
+      <td>0.0677</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>0.4000</td>
+      <td>0.6000</td>
+      <td>0.3550</td>
+      <td>0.0450</td>
+      <td>0.0648</td>
+      <td>0.5352</td>
+      <td>0.3682</td>
+      <td>0.0282</td>
+      <td>0.0516</td>
+      <td>0.5520</td>
+      <td>0.0315</td>
+      <td>0.8661</td>
+      <td>0.9017</td>
+      <td>0.0589</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>0.4500</td>
+      <td>0.5500</td>
+      <td>0.3988</td>
+      <td>0.0512</td>
+      <td>0.0600</td>
+      <td>0.4900</td>
+      <td>0.4127</td>
+      <td>0.0335</td>
+      <td>0.0461</td>
+      <td>0.5078</td>
+      <td>0.0326</td>
+      <td>0.8776</td>
+      <td>0.9115</td>
+      <td>0.0549</td>
+    </tr>
+    <tr>
+      <th>10</th>
+      <td>0.5000</td>
+      <td>0.5000</td>
+      <td>0.4439</td>
+      <td>0.0561</td>
+      <td>0.0547</td>
+      <td>0.4453</td>
+      <td>0.4580</td>
+      <td>0.0370</td>
+      <td>0.0405</td>
+      <td>0.4644</td>
+      <td>0.0333</td>
+      <td>0.8889</td>
+      <td>0.9217</td>
+      <td>0.0507</td>
+    </tr>
+    <tr>
+      <th>11</th>
+      <td>0.5500</td>
+      <td>0.4500</td>
+      <td>0.4895</td>
+      <td>0.0605</td>
+      <td>0.0498</td>
+      <td>0.4002</td>
+      <td>0.5014</td>
+      <td>0.0428</td>
+      <td>0.0379</td>
+      <td>0.4179</td>
+      <td>0.0337</td>
+      <td>0.8985</td>
+      <td>0.9252</td>
+      <td>0.0465</td>
+    </tr>
+    <tr>
+      <th>12</th>
+      <td>0.6000</td>
+      <td>0.4000</td>
+      <td>0.5318</td>
+      <td>0.0683</td>
+      <td>0.0437</td>
+      <td>0.3563</td>
+      <td>0.5446</td>
+      <td>0.0465</td>
+      <td>0.0309</td>
+      <td>0.3781</td>
+      <td>0.0333</td>
+      <td>0.9045</td>
+      <td>0.9336</td>
+      <td>0.0441</td>
+    </tr>
+    <tr>
+      <th>13</th>
+      <td>0.6500</td>
+      <td>0.3500</td>
+      <td>0.5755</td>
+      <td>0.0745</td>
+      <td>0.0392</td>
+      <td>0.3108</td>
+      <td>0.5843</td>
+      <td>0.0537</td>
+      <td>0.0304</td>
+      <td>0.3316</td>
+      <td>0.0327</td>
+      <td>0.9098</td>
+      <td>0.9326</td>
+      <td>0.0396</td>
+    </tr>
+    <tr>
+      <th>14</th>
+      <td>0.7000</td>
+      <td>0.3000</td>
+      <td>0.6219</td>
+      <td>0.0781</td>
+      <td>0.0326</td>
+      <td>0.2674</td>
+      <td>0.6292</td>
+      <td>0.0579</td>
+      <td>0.0253</td>
+      <td>0.2876</td>
+      <td>0.0313</td>
+      <td>0.9181</td>
+      <td>0.9379</td>
+      <td>0.0350</td>
+    </tr>
+    <tr>
+      <th>15</th>
+      <td>0.7500</td>
+      <td>0.2500</td>
+      <td>0.6641</td>
+      <td>0.0859</td>
+      <td>0.0273</td>
+      <td>0.2227</td>
+      <td>0.6708</td>
+      <td>0.0669</td>
+      <td>0.0206</td>
+      <td>0.2417</td>
+      <td>0.0300</td>
+      <td>0.9212</td>
+      <td>0.9389</td>
+      <td>0.0317</td>
+    </tr>
+    <tr>
+      <th>16</th>
+      <td>0.8000</td>
+      <td>0.2000</td>
+      <td>0.7084</td>
+      <td>0.0916</td>
+      <td>0.0226</td>
+      <td>0.1774</td>
+      <td>0.7120</td>
+      <td>0.0728</td>
+      <td>0.0190</td>
+      <td>0.1963</td>
+      <td>0.0286</td>
+      <td>0.9252</td>
+      <td>0.9393</td>
+      <td>0.0283</td>
+    </tr>
+    <tr>
+      <th>17</th>
+      <td>0.8500</td>
+      <td>0.1500</td>
+      <td>0.7536</td>
+      <td>0.0964</td>
+      <td>0.0167</td>
+      <td>0.1333</td>
+      <td>0.7533</td>
+      <td>0.0816</td>
+      <td>0.0170</td>
+      <td>0.1481</td>
+      <td>0.0251</td>
+      <td>0.9300</td>
+      <td>0.9384</td>
+      <td>0.0230</td>
+    </tr>
+    <tr>
+      <th>18</th>
+      <td>0.9000</td>
+      <td>0.1000</td>
+      <td>0.8009</td>
+      <td>0.0991</td>
+      <td>0.0109</td>
+      <td>0.0891</td>
+      <td>0.7979</td>
+      <td>0.0872</td>
+      <td>0.0139</td>
+      <td>0.1010</td>
+      <td>0.0225</td>
+      <td>0.9355</td>
+      <td>0.9402</td>
+      <td>0.0203</td>
+    </tr>
+    <tr>
+      <th>19</th>
+      <td>0.9500</td>
+      <td>0.0500</td>
+      <td>0.8437</td>
+      <td>0.1063</td>
+      <td>0.0057</td>
+      <td>0.0443</td>
+      <td>0.8364</td>
+      <td>0.0994</td>
+      <td>0.0130</td>
+      <td>0.0512</td>
+      <td>0.0181</td>
+      <td>0.9374</td>
+      <td>0.9367</td>
+      <td>0.0154</td>
+    </tr>
+    <tr>
+      <th>20</th>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.8886</td>
+      <td>0.1114</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.8790</td>
+      <td>0.1018</td>
+      <td>0.0096</td>
+      <td>0.0096</td>
+      <td>0.0096</td>
+      <td>0.9407</td>
+      <td>0.9400</td>
+      <td>0.0082</td>
+    </tr>
+  </tbody>
+</table>
+<div>&nbsp;</div>
+<div>multiclass</div>
+<table  class="dataframe">
+  <thead>
+    <tr>
+      <th></th>
+      <th colspan="2" halign="left">base</th>
+      <th colspan="4" halign="left">true</th>
+      <th colspan="4" halign="left">estim</th>
+      <th colspan="4" halign="left">errors</th>
+    </tr>
+    <tr>
+      <th></th>
+      <th>0</th>
+      <th>1</th>
+      <th>T0</th>
+      <th>F1</th>
+      <th>F0</th>
+      <th>T1</th>
+      <th>T0</th>
+      <th>F1</th>
+      <th>F0</th>
+      <th>T1</th>
+      <th>ae</th>
+      <th>f1_true</th>
+      <th>f1_estim</th>
+      <th>f1_dist</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0.0000</td>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.1100</td>
+      <td>0.8900</td>
+      <td>0.0057</td>
+      <td>0.0143</td>
+      <td>0.1012</td>
+      <td>0.8788</td>
+      <td>0.0125</td>
+      <td>NaN</td>
+      <td>0.0854</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>0.0500</td>
+      <td>0.9500</td>
+      <td>0.0448</td>
+      <td>0.0052</td>
+      <td>0.1050</td>
+      <td>0.8450</td>
+      <td>0.0505</td>
+      <td>0.0143</td>
+      <td>0.0970</td>
+      <td>0.8382</td>
+      <td>0.0197</td>
+      <td>0.4570</td>
+      <td>0.4651</td>
+      <td>0.1655</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>0.1000</td>
+      <td>0.9000</td>
+      <td>0.0890</td>
+      <td>0.0110</td>
+      <td>0.0981</td>
+      <td>0.8019</td>
+      <td>0.0982</td>
+      <td>0.0162</td>
+      <td>0.0865</td>
+      <td>0.7991</td>
+      <td>0.0222</td>
+      <td>0.6247</td>
+      <td>0.6541</td>
+      <td>0.1180</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>0.1500</td>
+      <td>0.8500</td>
+      <td>0.1329</td>
+      <td>0.0171</td>
+      <td>0.0948</td>
+      <td>0.7552</td>
+      <td>0.1452</td>
+      <td>0.0189</td>
+      <td>0.0803</td>
+      <td>0.7556</td>
+      <td>0.0274</td>
+      <td>0.7062</td>
+      <td>0.7421</td>
+      <td>0.1113</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>0.2000</td>
+      <td>0.8000</td>
+      <td>0.1776</td>
+      <td>0.0224</td>
+      <td>0.0870</td>
+      <td>0.7130</td>
+      <td>0.1919</td>
+      <td>0.0197</td>
+      <td>0.0706</td>
+      <td>0.7177</td>
+      <td>0.0280</td>
+      <td>0.7661</td>
+      <td>0.8081</td>
+      <td>0.0927</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>0.2500</td>
+      <td>0.7500</td>
+      <td>0.2225</td>
+      <td>0.0275</td>
+      <td>0.0837</td>
+      <td>0.6663</td>
+      <td>0.2378</td>
+      <td>0.0224</td>
+      <td>0.0669</td>
+      <td>0.6729</td>
+      <td>0.0306</td>
+      <td>0.8013</td>
+      <td>0.8408</td>
+      <td>0.0818</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>0.3000</td>
+      <td>0.7000</td>
+      <td>0.2668</td>
+      <td>0.0332</td>
+      <td>0.0760</td>
+      <td>0.6240</td>
+      <td>0.2824</td>
+      <td>0.0256</td>
+      <td>0.0586</td>
+      <td>0.6334</td>
+      <td>0.0315</td>
+      <td>0.8306</td>
+      <td>0.8693</td>
+      <td>0.0711</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>0.3500</td>
+      <td>0.6500</td>
+      <td>0.3100</td>
+      <td>0.0400</td>
+      <td>0.0712</td>
+      <td>0.5788</td>
+      <td>0.3296</td>
+      <td>0.0295</td>
+      <td>0.0503</td>
+      <td>0.5905</td>
+      <td>0.0339</td>
+      <td>0.8480</td>
+      <td>0.8913</td>
+      <td>0.0696</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>0.4000</td>
+      <td>0.6000</td>
+      <td>0.3550</td>
+      <td>0.0450</td>
+      <td>0.0648</td>
+      <td>0.5352</td>
+      <td>0.3726</td>
+      <td>0.0312</td>
+      <td>0.0462</td>
+      <td>0.5501</td>
+      <td>0.0336</td>
+      <td>0.8661</td>
+      <td>0.9055</td>
+      <td>0.0610</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>0.4500</td>
+      <td>0.5500</td>
+      <td>0.3988</td>
+      <td>0.0512</td>
+      <td>0.0600</td>
+      <td>0.4900</td>
+      <td>0.4160</td>
+      <td>0.0355</td>
+      <td>0.0422</td>
+      <td>0.5064</td>
+      <td>0.0339</td>
+      <td>0.8776</td>
+      <td>0.9141</td>
+      <td>0.0554</td>
+    </tr>
+    <tr>
+      <th>10</th>
+      <td>0.5000</td>
+      <td>0.5000</td>
+      <td>0.4439</td>
+      <td>0.0561</td>
+      <td>0.0547</td>
+      <td>0.4453</td>
+      <td>0.4608</td>
+      <td>0.0373</td>
+      <td>0.0382</td>
+      <td>0.4637</td>
+      <td>0.0346</td>
+      <td>0.8889</td>
+      <td>0.9241</td>
+      <td>0.0516</td>
+    </tr>
+    <tr>
+      <th>11</th>
+      <td>0.5500</td>
+      <td>0.4500</td>
+      <td>0.4895</td>
+      <td>0.0605</td>
+      <td>0.0498</td>
+      <td>0.4002</td>
+      <td>0.5044</td>
+      <td>0.0425</td>
+      <td>0.0355</td>
+      <td>0.4176</td>
+      <td>0.0346</td>
+      <td>0.8985</td>
+      <td>0.9280</td>
+      <td>0.0471</td>
+    </tr>
+    <tr>
+      <th>12</th>
+      <td>0.6000</td>
+      <td>0.4000</td>
+      <td>0.5318</td>
+      <td>0.0683</td>
+      <td>0.0437</td>
+      <td>0.3563</td>
+      <td>0.5468</td>
+      <td>0.0452</td>
+      <td>0.0296</td>
+      <td>0.3783</td>
+      <td>0.0344</td>
+      <td>0.9045</td>
+      <td>0.9359</td>
+      <td>0.0448</td>
+    </tr>
+    <tr>
+      <th>13</th>
+      <td>0.6500</td>
+      <td>0.3500</td>
+      <td>0.5755</td>
+      <td>0.0745</td>
+      <td>0.0392</td>
+      <td>0.3108</td>
+      <td>0.5866</td>
+      <td>0.0518</td>
+      <td>0.0293</td>
+      <td>0.3323</td>
+      <td>0.0338</td>
+      <td>0.9098</td>
+      <td>0.9352</td>
+      <td>0.0403</td>
+    </tr>
+    <tr>
+      <th>14</th>
+      <td>0.7000</td>
+      <td>0.3000</td>
+      <td>0.6219</td>
+      <td>0.0781</td>
+      <td>0.0326</td>
+      <td>0.2674</td>
+      <td>0.6312</td>
+      <td>0.0534</td>
+      <td>0.0258</td>
+      <td>0.2896</td>
+      <td>0.0331</td>
+      <td>0.9181</td>
+      <td>0.9408</td>
+      <td>0.0367</td>
+    </tr>
+    <tr>
+      <th>15</th>
+      <td>0.7500</td>
+      <td>0.2500</td>
+      <td>0.6641</td>
+      <td>0.0859</td>
+      <td>0.0273</td>
+      <td>0.2227</td>
+      <td>0.6730</td>
+      <td>0.0619</td>
+      <td>0.0207</td>
+      <td>0.2445</td>
+      <td>0.0322</td>
+      <td>0.9212</td>
+      <td>0.9423</td>
+      <td>0.0335</td>
+    </tr>
+    <tr>
+      <th>16</th>
+      <td>0.8000</td>
+      <td>0.2000</td>
+      <td>0.7084</td>
+      <td>0.0916</td>
+      <td>0.0226</td>
+      <td>0.1774</td>
+      <td>0.7141</td>
+      <td>0.0657</td>
+      <td>0.0200</td>
+      <td>0.2001</td>
+      <td>0.0309</td>
+      <td>0.9252</td>
+      <td>0.9431</td>
+      <td>0.0304</td>
+    </tr>
+    <tr>
+      <th>17</th>
+      <td>0.8500</td>
+      <td>0.1500</td>
+      <td>0.7536</td>
+      <td>0.0964</td>
+      <td>0.0167</td>
+      <td>0.1333</td>
+      <td>0.7556</td>
+      <td>0.0756</td>
+      <td>0.0171</td>
+      <td>0.1517</td>
+      <td>0.0274</td>
+      <td>0.9300</td>
+      <td>0.9420</td>
+      <td>0.0247</td>
+    </tr>
+    <tr>
+      <th>18</th>
+      <td>0.9000</td>
+      <td>0.1000</td>
+      <td>0.8009</td>
+      <td>0.0991</td>
+      <td>0.0109</td>
+      <td>0.0891</td>
+      <td>0.8000</td>
+      <td>0.0799</td>
+      <td>0.0150</td>
+      <td>0.1050</td>
+      <td>0.0250</td>
+      <td>0.9355</td>
+      <td>0.9437</td>
+      <td>0.0223</td>
+    </tr>
+    <tr>
+      <th>19</th>
+      <td>0.9500</td>
+      <td>0.0500</td>
+      <td>0.8437</td>
+      <td>0.1063</td>
+      <td>0.0057</td>
+      <td>0.0443</td>
+      <td>0.8389</td>
+      <td>0.0916</td>
+      <td>0.0140</td>
+      <td>0.0555</td>
+      <td>0.0209</td>
+      <td>0.9374</td>
+      <td>0.9405</td>
+      <td>0.0167</td>
+    </tr>
+    <tr>
+      <th>20</th>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.8886</td>
+      <td>0.1114</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.8811</td>
+      <td>0.0965</td>
+      <td>0.0108</td>
+      <td>0.0116</td>
+      <td>0.0135</td>
+      <td>0.9407</td>
+      <td>0.9422</td>
+      <td>0.0108</td>
+    </tr>
+  </tbody>
+</table>
+</body>
+</html>
\ No newline at end of file
diff --git a/out_spambase.html b/out_spambase.html
new file mode 100644
index 0000000..34d9932
--- /dev/null
+++ b/out_spambase.html
@@ -0,0 +1,802 @@
+<html>
+<head>
+  <style>
+    table, th, td {
+      border: 1px solid black;
+      border-collapse: collapse;
+    }
+    th, td {
+      padding: 4px;
+    }
+    tr:hover{
+      background-color: lightgreen;
+    }
+  </style>
+</head>
+<body>
+<div>spambase</div>
+<div>#instances=4601, type=<class 'numpy.ndarray'>, #features=57, #classes=[0 1], prevs=[0.606, 0.394]</div>
+<div>Tests:</div>
+<div>protocol=APP</div>
+<div>n_prevalences=21</div>
+<div>repreats=1000</div>
+<div>&nbsp;</div>
+<div>binary</div>
+<table class="dataframe">
+  <thead>
+    <tr>
+      <th></th>
+      <th colspan="2" halign="left">base</th>
+      <th colspan="4" halign="left">true</th>
+      <th colspan="4" halign="left">estim</th>
+      <th colspan="4" halign="left">errors</th>
+    </tr>
+    <tr>
+      <th></th>
+      <th>0</th>
+      <th>1</th>
+      <th>T0</th>
+      <th>F1</th>
+      <th>F0</th>
+      <th>T1</th>
+      <th>T0</th>
+      <th>F1</th>
+      <th>F0</th>
+      <th>T1</th>
+      <th>ae</th>
+      <th>f1_true</th>
+      <th>f1_estim</th>
+      <th>f1_dist</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0.0000</td>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.0949</td>
+      <td>0.9052</td>
+      <td>0.0067</td>
+      <td>0.0012</td>
+      <td>0.0882</td>
+      <td>0.9040</td>
+      <td>0.0039</td>
+      <td>NaN</td>
+      <td>0.1154</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>0.0500</td>
+      <td>0.9500</td>
+      <td>0.0470</td>
+      <td>0.0030</td>
+      <td>0.0908</td>
+      <td>0.8592</td>
+      <td>0.0584</td>
+      <td>0.0020</td>
+      <td>0.0794</td>
+      <td>0.8601</td>
+      <td>0.0114</td>
+      <td>0.5095</td>
+      <td>0.5828</td>
+      <td>0.1418</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>0.1000</td>
+      <td>0.9000</td>
+      <td>0.0940</td>
+      <td>0.0060</td>
+      <td>0.0867</td>
+      <td>0.8133</td>
+      <td>0.1090</td>
+      <td>0.0030</td>
+      <td>0.0717</td>
+      <td>0.8163</td>
+      <td>0.0147</td>
+      <td>0.6748</td>
+      <td>0.7417</td>
+      <td>0.1057</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>0.1500</td>
+      <td>0.8500</td>
+      <td>0.1410</td>
+      <td>0.0090</td>
+      <td>0.0817</td>
+      <td>0.7683</td>
+      <td>0.1585</td>
+      <td>0.0039</td>
+      <td>0.0642</td>
+      <td>0.7733</td>
+      <td>0.0180</td>
+      <td>0.7593</td>
+      <td>0.8205</td>
+      <td>0.0926</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>0.2000</td>
+      <td>0.8000</td>
+      <td>0.1882</td>
+      <td>0.0118</td>
+      <td>0.0761</td>
+      <td>0.7238</td>
+      <td>0.2081</td>
+      <td>0.0066</td>
+      <td>0.0562</td>
+      <td>0.7291</td>
+      <td>0.0203</td>
+      <td>0.8121</td>
+      <td>0.8676</td>
+      <td>0.0762</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>0.2500</td>
+      <td>0.7500</td>
+      <td>0.2360</td>
+      <td>0.0140</td>
+      <td>0.0707</td>
+      <td>0.6794</td>
+      <td>0.2564</td>
+      <td>0.0073</td>
+      <td>0.0503</td>
+      <td>0.6860</td>
+      <td>0.0212</td>
+      <td>0.8491</td>
+      <td>0.8982</td>
+      <td>0.0662</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>0.3000</td>
+      <td>0.7000</td>
+      <td>0.2828</td>
+      <td>0.0173</td>
+      <td>0.0657</td>
+      <td>0.6343</td>
+      <td>0.3051</td>
+      <td>0.0093</td>
+      <td>0.0434</td>
+      <td>0.6422</td>
+      <td>0.0236</td>
+      <td>0.8727</td>
+      <td>0.9202</td>
+      <td>0.0622</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>0.3500</td>
+      <td>0.6500</td>
+      <td>0.3293</td>
+      <td>0.0207</td>
+      <td>0.0630</td>
+      <td>0.5870</td>
+      <td>0.3521</td>
+      <td>0.0106</td>
+      <td>0.0402</td>
+      <td>0.5971</td>
+      <td>0.0244</td>
+      <td>0.8877</td>
+      <td>0.9325</td>
+      <td>0.0556</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>0.4000</td>
+      <td>0.6000</td>
+      <td>0.3759</td>
+      <td>0.0241</td>
+      <td>0.0563</td>
+      <td>0.5438</td>
+      <td>0.3975</td>
+      <td>0.0149</td>
+      <td>0.0347</td>
+      <td>0.5529</td>
+      <td>0.0252</td>
+      <td>0.9037</td>
+      <td>0.9411</td>
+      <td>0.0485</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>0.4500</td>
+      <td>0.5500</td>
+      <td>0.4246</td>
+      <td>0.0254</td>
+      <td>0.0533</td>
+      <td>0.4967</td>
+      <td>0.4465</td>
+      <td>0.0157</td>
+      <td>0.0315</td>
+      <td>0.5064</td>
+      <td>0.0262</td>
+      <td>0.9153</td>
+      <td>0.9499</td>
+      <td>0.0464</td>
+    </tr>
+    <tr>
+      <th>10</th>
+      <td>0.5000</td>
+      <td>0.5000</td>
+      <td>0.4718</td>
+      <td>0.0282</td>
+      <td>0.0475</td>
+      <td>0.4525</td>
+      <td>0.4904</td>
+      <td>0.0180</td>
+      <td>0.0289</td>
+      <td>0.4628</td>
+      <td>0.0255</td>
+      <td>0.9258</td>
+      <td>0.9543</td>
+      <td>0.0397</td>
+    </tr>
+    <tr>
+      <th>11</th>
+      <td>0.5500</td>
+      <td>0.4500</td>
+      <td>0.5181</td>
+      <td>0.0319</td>
+      <td>0.0427</td>
+      <td>0.4073</td>
+      <td>0.5344</td>
+      <td>0.0233</td>
+      <td>0.0264</td>
+      <td>0.4159</td>
+      <td>0.0261</td>
+      <td>0.9328</td>
+      <td>0.9556</td>
+      <td>0.0365</td>
+    </tr>
+    <tr>
+      <th>12</th>
+      <td>0.6000</td>
+      <td>0.4000</td>
+      <td>0.5655</td>
+      <td>0.0345</td>
+      <td>0.0377</td>
+      <td>0.3623</td>
+      <td>0.5803</td>
+      <td>0.0249</td>
+      <td>0.0229</td>
+      <td>0.3719</td>
+      <td>0.0254</td>
+      <td>0.9400</td>
+      <td>0.9606</td>
+      <td>0.0314</td>
+    </tr>
+    <tr>
+      <th>13</th>
+      <td>0.6500</td>
+      <td>0.3500</td>
+      <td>0.6124</td>
+      <td>0.0376</td>
+      <td>0.0327</td>
+      <td>0.3173</td>
+      <td>0.6240</td>
+      <td>0.0280</td>
+      <td>0.0211</td>
+      <td>0.3268</td>
+      <td>0.0248</td>
+      <td>0.9457</td>
+      <td>0.9622</td>
+      <td>0.0294</td>
+    </tr>
+    <tr>
+      <th>14</th>
+      <td>0.7000</td>
+      <td>0.3000</td>
+      <td>0.6593</td>
+      <td>0.0407</td>
+      <td>0.0286</td>
+      <td>0.2714</td>
+      <td>0.6680</td>
+      <td>0.0326</td>
+      <td>0.0199</td>
+      <td>0.2794</td>
+      <td>0.0246</td>
+      <td>0.9500</td>
+      <td>0.9623</td>
+      <td>0.0268</td>
+    </tr>
+    <tr>
+      <th>15</th>
+      <td>0.7500</td>
+      <td>0.2500</td>
+      <td>0.7054</td>
+      <td>0.0446</td>
+      <td>0.0242</td>
+      <td>0.2258</td>
+      <td>0.7113</td>
+      <td>0.0367</td>
+      <td>0.0182</td>
+      <td>0.2337</td>
+      <td>0.0244</td>
+      <td>0.9534</td>
+      <td>0.9630</td>
+      <td>0.0240</td>
+    </tr>
+    <tr>
+      <th>16</th>
+      <td>0.8000</td>
+      <td>0.2000</td>
+      <td>0.7528</td>
+      <td>0.0472</td>
+      <td>0.0192</td>
+      <td>0.1808</td>
+      <td>0.7564</td>
+      <td>0.0394</td>
+      <td>0.0156</td>
+      <td>0.1887</td>
+      <td>0.0234</td>
+      <td>0.9577</td>
+      <td>0.9651</td>
+      <td>0.0222</td>
+    </tr>
+    <tr>
+      <th>17</th>
+      <td>0.8500</td>
+      <td>0.1500</td>
+      <td>0.8007</td>
+      <td>0.0493</td>
+      <td>0.0143</td>
+      <td>0.1357</td>
+      <td>0.8004</td>
+      <td>0.0442</td>
+      <td>0.0147</td>
+      <td>0.1407</td>
+      <td>0.0214</td>
+      <td>0.9617</td>
+      <td>0.9646</td>
+      <td>0.0192</td>
+    </tr>
+    <tr>
+      <th>18</th>
+      <td>0.9000</td>
+      <td>0.1000</td>
+      <td>0.8478</td>
+      <td>0.0522</td>
+      <td>0.0094</td>
+      <td>0.0906</td>
+      <td>0.8445</td>
+      <td>0.0472</td>
+      <td>0.0127</td>
+      <td>0.0956</td>
+      <td>0.0191</td>
+      <td>0.9648</td>
+      <td>0.9658</td>
+      <td>0.0163</td>
+    </tr>
+    <tr>
+      <th>19</th>
+      <td>0.9500</td>
+      <td>0.0500</td>
+      <td>0.8945</td>
+      <td>0.0555</td>
+      <td>0.0050</td>
+      <td>0.0450</td>
+      <td>0.8883</td>
+      <td>0.0512</td>
+      <td>0.0112</td>
+      <td>0.0493</td>
+      <td>0.0170</td>
+      <td>0.9672</td>
+      <td>0.9661</td>
+      <td>0.0141</td>
+    </tr>
+    <tr>
+      <th>20</th>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.9421</td>
+      <td>0.0579</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.9306</td>
+      <td>0.0476</td>
+      <td>0.0115</td>
+      <td>0.0103</td>
+      <td>0.0109</td>
+      <td>0.9701</td>
+      <td>0.9691</td>
+      <td>0.0081</td>
+    </tr>
+  </tbody>
+</table>
+<div>&nbsp;</div>
+<div>multiclass</div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr>
+      <th></th>
+      <th colspan="2" halign="left">base</th>
+      <th colspan="4" halign="left">true</th>
+      <th colspan="4" halign="left">estim</th>
+      <th colspan="4" halign="left">errors</th>
+    </tr>
+    <tr>
+      <th></th>
+      <th>0</th>
+      <th>1</th>
+      <th>T0</th>
+      <th>F1</th>
+      <th>F0</th>
+      <th>T1</th>
+      <th>T0</th>
+      <th>F1</th>
+      <th>F0</th>
+      <th>T1</th>
+      <th>ae</th>
+      <th>f1_true</th>
+      <th>f1_estim</th>
+      <th>f1_dist</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0.0000</td>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.0949</td>
+      <td>0.9052</td>
+      <td>0.0008</td>
+      <td>0.0001</td>
+      <td>0.0202</td>
+      <td>0.9789</td>
+      <td>0.0375</td>
+      <td>NaN</td>
+      <td>0.0949</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>0.0500</td>
+      <td>0.9500</td>
+      <td>0.0470</td>
+      <td>0.0030</td>
+      <td>0.0908</td>
+      <td>0.8592</td>
+      <td>0.0460</td>
+      <td>0.0001</td>
+      <td>0.0329</td>
+      <td>0.9209</td>
+      <td>0.0368</td>
+      <td>0.5095</td>
+      <td>0.7394</td>
+      <td>0.3362</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>0.1000</td>
+      <td>0.9000</td>
+      <td>0.0940</td>
+      <td>0.0060</td>
+      <td>0.0867</td>
+      <td>0.8133</td>
+      <td>0.0971</td>
+      <td>0.0001</td>
+      <td>0.0379</td>
+      <td>0.8650</td>
+      <td>0.0358</td>
+      <td>0.6748</td>
+      <td>0.8416</td>
+      <td>0.2255</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>0.1500</td>
+      <td>0.8500</td>
+      <td>0.1410</td>
+      <td>0.0090</td>
+      <td>0.0817</td>
+      <td>0.7683</td>
+      <td>0.1459</td>
+      <td>0.0001</td>
+      <td>0.0452</td>
+      <td>0.8088</td>
+      <td>0.0342</td>
+      <td>0.7593</td>
+      <td>0.8626</td>
+      <td>0.1647</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>0.2000</td>
+      <td>0.8000</td>
+      <td>0.1882</td>
+      <td>0.0118</td>
+      <td>0.0761</td>
+      <td>0.7238</td>
+      <td>0.1985</td>
+      <td>0.0001</td>
+      <td>0.0499</td>
+      <td>0.7515</td>
+      <td>0.0324</td>
+      <td>0.8121</td>
+      <td>0.8892</td>
+      <td>0.1246</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>0.2500</td>
+      <td>0.7500</td>
+      <td>0.2360</td>
+      <td>0.0140</td>
+      <td>0.0707</td>
+      <td>0.6794</td>
+      <td>0.2519</td>
+      <td>0.0001</td>
+      <td>0.0511</td>
+      <td>0.6970</td>
+      <td>0.0318</td>
+      <td>0.8491</td>
+      <td>0.9077</td>
+      <td>0.0996</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>0.3000</td>
+      <td>0.7000</td>
+      <td>0.2828</td>
+      <td>0.0173</td>
+      <td>0.0657</td>
+      <td>0.6343</td>
+      <td>0.3042</td>
+      <td>0.0001</td>
+      <td>0.0513</td>
+      <td>0.6444</td>
+      <td>0.0319</td>
+      <td>0.8727</td>
+      <td>0.9216</td>
+      <td>0.0832</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>0.3500</td>
+      <td>0.6500</td>
+      <td>0.3293</td>
+      <td>0.0207</td>
+      <td>0.0630</td>
+      <td>0.5870</td>
+      <td>0.3606</td>
+      <td>0.0001</td>
+      <td>0.0539</td>
+      <td>0.5854</td>
+      <td>0.0350</td>
+      <td>0.8877</td>
+      <td>0.9298</td>
+      <td>0.0756</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>0.4000</td>
+      <td>0.6000</td>
+      <td>0.3759</td>
+      <td>0.0241</td>
+      <td>0.0563</td>
+      <td>0.5438</td>
+      <td>0.4070</td>
+      <td>0.0001</td>
+      <td>0.0580</td>
+      <td>0.5349</td>
+      <td>0.0365</td>
+      <td>0.9037</td>
+      <td>0.9326</td>
+      <td>0.0666</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>0.4500</td>
+      <td>0.5500</td>
+      <td>0.4246</td>
+      <td>0.0254</td>
+      <td>0.0533</td>
+      <td>0.4967</td>
+      <td>0.4652</td>
+      <td>0.0001</td>
+      <td>0.0574</td>
+      <td>0.4773</td>
+      <td>0.0388</td>
+      <td>0.9153</td>
+      <td>0.9412</td>
+      <td>0.0588</td>
+    </tr>
+    <tr>
+      <th>10</th>
+      <td>0.5000</td>
+      <td>0.5000</td>
+      <td>0.4718</td>
+      <td>0.0282</td>
+      <td>0.0475</td>
+      <td>0.4525</td>
+      <td>0.5159</td>
+      <td>0.0001</td>
+      <td>0.0605</td>
+      <td>0.4235</td>
+      <td>0.0417</td>
+      <td>0.9258</td>
+      <td>0.9439</td>
+      <td>0.0526</td>
+    </tr>
+    <tr>
+      <th>11</th>
+      <td>0.5500</td>
+      <td>0.4500</td>
+      <td>0.5181</td>
+      <td>0.0319</td>
+      <td>0.0427</td>
+      <td>0.4073</td>
+      <td>0.5681</td>
+      <td>0.0001</td>
+      <td>0.0574</td>
+      <td>0.3744</td>
+      <td>0.0437</td>
+      <td>0.9328</td>
+      <td>0.9511</td>
+      <td>0.0467</td>
+    </tr>
+    <tr>
+      <th>12</th>
+      <td>0.6000</td>
+      <td>0.4000</td>
+      <td>0.5655</td>
+      <td>0.0345</td>
+      <td>0.0377</td>
+      <td>0.3623</td>
+      <td>0.6211</td>
+      <td>0.0001</td>
+      <td>0.0552</td>
+      <td>0.3236</td>
+      <td>0.0463</td>
+      <td>0.9400</td>
+      <td>0.9568</td>
+      <td>0.0426</td>
+    </tr>
+    <tr>
+      <th>13</th>
+      <td>0.6500</td>
+      <td>0.3500</td>
+      <td>0.6124</td>
+      <td>0.0376</td>
+      <td>0.0327</td>
+      <td>0.3173</td>
+      <td>0.6710</td>
+      <td>0.0001</td>
+      <td>0.0575</td>
+      <td>0.2714</td>
+      <td>0.0496</td>
+      <td>0.9457</td>
+      <td>0.9582</td>
+      <td>0.0385</td>
+    </tr>
+    <tr>
+      <th>14</th>
+      <td>0.7000</td>
+      <td>0.3000</td>
+      <td>0.6593</td>
+      <td>0.0407</td>
+      <td>0.0286</td>
+      <td>0.2714</td>
+      <td>0.7240</td>
+      <td>0.0001</td>
+      <td>0.0541</td>
+      <td>0.2217</td>
+      <td>0.0515</td>
+      <td>0.9500</td>
+      <td>0.9634</td>
+      <td>0.0345</td>
+    </tr>
+    <tr>
+      <th>15</th>
+      <td>0.7500</td>
+      <td>0.2500</td>
+      <td>0.7054</td>
+      <td>0.0446</td>
+      <td>0.0242</td>
+      <td>0.2258</td>
+      <td>0.7750</td>
+      <td>0.0001</td>
+      <td>0.0476</td>
+      <td>0.1773</td>
+      <td>0.0523</td>
+      <td>0.9534</td>
+      <td>0.9697</td>
+      <td>0.0337</td>
+    </tr>
+    <tr>
+      <th>16</th>
+      <td>0.8000</td>
+      <td>0.2000</td>
+      <td>0.7528</td>
+      <td>0.0472</td>
+      <td>0.0192</td>
+      <td>0.1808</td>
+      <td>0.8212</td>
+      <td>0.0001</td>
+      <td>0.0469</td>
+      <td>0.1318</td>
+      <td>0.0526</td>
+      <td>0.9577</td>
+      <td>0.9717</td>
+      <td>0.0296</td>
+    </tr>
+    <tr>
+      <th>17</th>
+      <td>0.8500</td>
+      <td>0.1500</td>
+      <td>0.8007</td>
+      <td>0.0493</td>
+      <td>0.0143</td>
+      <td>0.1357</td>
+      <td>0.8673</td>
+      <td>0.0001</td>
+      <td>0.0443</td>
+      <td>0.0882</td>
+      <td>0.0524</td>
+      <td>0.9617</td>
+      <td>0.9745</td>
+      <td>0.0272</td>
+    </tr>
+    <tr>
+      <th>18</th>
+      <td>0.9000</td>
+      <td>0.1000</td>
+      <td>0.8478</td>
+      <td>0.0522</td>
+      <td>0.0094</td>
+      <td>0.0906</td>
+      <td>0.9127</td>
+      <td>0.0001</td>
+      <td>0.0366</td>
+      <td>0.0506</td>
+      <td>0.0489</td>
+      <td>0.9648</td>
+      <td>0.9799</td>
+      <td>0.0250</td>
+    </tr>
+    <tr>
+      <th>19</th>
+      <td>0.9500</td>
+      <td>0.0500</td>
+      <td>0.8945</td>
+      <td>0.0555</td>
+      <td>0.0050</td>
+      <td>0.0450</td>
+      <td>0.9515</td>
+      <td>0.0001</td>
+      <td>0.0275</td>
+      <td>0.0208</td>
+      <td>0.0423</td>
+      <td>0.9672</td>
+      <td>0.9854</td>
+      <td>0.0235</td>
+    </tr>
+    <tr>
+      <th>20</th>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.9421</td>
+      <td>0.0579</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.9762</td>
+      <td>0.0001</td>
+      <td>0.0190</td>
+      <td>0.0047</td>
+      <td>0.0301</td>
+      <td>0.9701</td>
+      <td>0.9901</td>
+      <td>0.0222</td>
+    </tr>
+  </tbody>
+</table>
+</body>
+</html>
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
index 7988285..c1e99a5 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -100,6 +100,78 @@ mypy = ["contourpy[bokeh]", "docutils-stubs", "mypy (==0.991)", "types-Pillow"]
 test = ["Pillow", "matplotlib", "pytest"]
 test-no-images = ["pytest"]
 
+[[package]]
+name = "coverage"
+version = "7.2.7"
+description = "Code coverage measurement for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "coverage-7.2.7.tar.gz", hash = "sha256:924d94291ca674905fe9481f12294eb11f2d3d3fd1adb20314ba89e94f44ed59"},
+]
+
+[package.extras]
+toml = ["tomli"]
+
 [[package]]
 name = "cycler"
 version = "0.11.0"
@@ -147,6 +219,23 @@ files = [
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
 
+[[package]]
+name = "jinja2"
+version = "3.1.2"
+description = "A very fast and expressive template engine."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "Jinja2-3.1.2-py3-none-any.whl", hash = "sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61"},
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+]
+
+[package.dependencies]
+MarkupSafe = ">=2.0"
+
+[package.extras]
+i18n = ["Babel (>=2.7)"]
+
 [[package]]
 name = "joblib"
 version = "1.2.0"
@@ -235,6 +324,65 @@ files = [
     {file = "kiwisolver-1.4.4.tar.gz", hash = "sha256:d41997519fcba4a1e46eb4a2fe31bc12f0ff957b2b81bac28db24744f333e955"},
 ]
 
+[[package]]
+name = "markupsafe"
+version = "2.1.3"
+description = "Safely add untrusted strings to HTML/XML markup."
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "MarkupSafe-2.1.3.tar.gz", hash = "sha256:af598ed32d6ae86f1b747b82783958b1a4ab8f617b06fe68795c7f026abbdcad"},
+]
+
 [[package]]
 name = "matplotlib"
 version = "3.7.1"
@@ -504,6 +652,65 @@ files = [
 dev = ["pre-commit", "tox"]
 testing = ["pytest", "pytest-benchmark"]
 
+[[package]]
+name = "pyarrow"
+version = "12.0.1"
+description = "Python library for Apache Arrow"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "pyarrow-12.0.1-cp310-cp310-macosx_10_14_x86_64.whl", hash = "sha256:6d288029a94a9bb5407ceebdd7110ba398a00412c5b0155ee9813a40d246c5df"},
+    {file = "pyarrow-12.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:345e1828efdbd9aa4d4de7d5676778aba384a2c3add896d995b23d368e60e5af"},
+    {file = "pyarrow-12.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8d6009fdf8986332b2169314da482baed47ac053311c8934ac6651e614deacd6"},
+    {file = "pyarrow-12.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2d3c4cbbf81e6dd23fe921bc91dc4619ea3b79bc58ef10bce0f49bdafb103daf"},
+    {file = "pyarrow-12.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:cdacf515ec276709ac8042c7d9bd5be83b4f5f39c6c037a17a60d7ebfd92c890"},
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+    {file = "pyarrow-12.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6251e38470da97a5b2e00de5c6a049149f7b2bd62f12fa5dbb9ac674119ba71a"},
+    {file = "pyarrow-12.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:3de26da901216149ce086920547dfff5cd22818c9eab67ebc41e863a5883bac7"},
+    {file = "pyarrow-12.0.1.tar.gz", hash = "sha256:cce317fc96e5b71107bf1f9f184d5e54e2bd14bbf3f9a3d62819961f0af86fec"},
+]
+
+[package.dependencies]
+numpy = ">=1.16.6"
+
+[[package]]
+name = "pylance"
+version = "0.5.9"
+description = "python wrapper for lance-rs"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pylance-0.5.9-cp38-abi3-macosx_10_15_x86_64.whl", hash = "sha256:6d5395f8a400caa152b9dc8d1f979b67c3003cbe54e038a53b33127f9a0c96da"},
+    {file = "pylance-0.5.9-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:b099a7fa01a96dc6a3d7413890a3c06c6837563100f6e71311916dbd24f1b26e"},
+    {file = "pylance-0.5.9-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:293520fada417e653085118d296ba011024ad0cfbbb1e60ee025b14aae2c0178"},
+    {file = "pylance-0.5.9-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:22bbb04024302996268bef98e190d62960f62293e2b5606be276d801cdfb13f3"},
+    {file = "pylance-0.5.9-cp38-abi3-win_amd64.whl", hash = "sha256:810d4020a65b8d001352cc69c751602412f73c498b1ac0cdb408f00cb493a2b6"},
+]
+
+[package.dependencies]
+numpy = ">=1.22"
+pandas = ">=1.4"
+pyarrow = ">=10"
+
+[package.extras]
+tests = ["duckdb", "ml_dtypes", "polars[pandas,pyarrow]", "pytest"]
+
 [[package]]
 name = "pyparsing"
 version = "3.0.9"
@@ -538,6 +745,41 @@ pluggy = ">=0.12,<2.0"
 [package.extras]
 testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
+[[package]]
+name = "pytest-cov"
+version = "4.1.0"
+description = "Pytest plugin for measuring coverage."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "pytest-cov-4.1.0.tar.gz", hash = "sha256:3904b13dfbfec47f003b8e77fd5b589cd11904a21ddf1ab38a64f204d6a10ef6"},
+    {file = "pytest_cov-4.1.0-py3-none-any.whl", hash = "sha256:6ba70b9e97e69fcc3fb45bfeab2d0a138fb65c4d0d6a41ef33983ad114be8c3a"},
+]
+
+[package.dependencies]
+coverage = {version = ">=5.2.1", extras = ["toml"]}
+pytest = ">=4.6"
+
+[package.extras]
+testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtualenv"]
+
+[[package]]
+name = "pytest-mock"
+version = "3.11.1"
+description = "Thin-wrapper around the mock package for easier use with pytest"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "pytest-mock-3.11.1.tar.gz", hash = "sha256:7f6b125602ac6d743e523ae0bfa71e1a697a2f5534064528c6ff84c2f7c2fc7f"},
+    {file = "pytest_mock-3.11.1-py3-none-any.whl", hash = "sha256:21c279fff83d70763b05f8874cc9cfb3fcacd6d354247a976f9529d19f9acf39"},
+]
+
+[package.dependencies]
+pytest = ">=5.0"
+
+[package.extras]
+dev = ["pre-commit", "pytest-asyncio", "tox"]
+
 [[package]]
 name = "python-dateutil"
 version = "2.8.2"
@@ -735,4 +977,4 @@ test = ["pytest", "pytest-cov"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
-content-hash = "834ffb619893a1fb006e1b5a3213cc772117c9000e719b95a4478f74fd5d0066"
+content-hash = "72e3afd9a24b88fc8a8f5f55e1c408f65090fce9015a442f6f41638191276b6f"
diff --git a/pyproject.toml b/pyproject.toml
index a6297fd..49bfad4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,13 +9,21 @@ readme = "README.md"
 python = "^3.11"
 quapy = "^0.1.7"
 pandas = "^2.0.3"
+jinja2 = "^3.1.2"
 
 [tool.poetry.scripts]
-main = "quacc.main:main"
+multi = "quacc.main:estimate_multiclass"
+bin = "quacc.main:estimate_binary"
 
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.4.0"
+pylance = "^0.5.9"
+pytest-mock = "^3.11.1"
+pytest-cov = "^4.1.0"
+
+[tool.pytest.ini_options]
+addopts = "--cov=quacc"
 
 [build-system]
 requires = ["poetry-core"]
diff --git a/quacc/data.py b/quacc/data.py
index 8f9b53c..fd1e3c3 100644
--- a/quacc/data.py
+++ b/quacc/data.py
@@ -46,36 +46,77 @@ class ExtendedCollection(LabelledCollection):
 
     def split_by_pred(self):
         _ncl = int(math.sqrt(self.n_classes))
-        _indexes = ExtendedCollection.split_index_by_pred(_ncl, self.instances)
-        return [
-            ExtendedCollection(
-                self.instances[ind] if len(ind) > 0 else np.asarray([], dtype=int),
-                np.asarray(
-                    [
-                        ExClassManager.get_true(_ncl, lbl)
-                        for lbl in (self.labels[ind] if len(ind) > 0 else [])
-                    ],
-                    dtype=int,
-                ),
-                classes=range(0, _ncl),
+        _indexes = ExtendedCollection._split_index_by_pred(_ncl, self.instances)
+        if isinstance(self.instances, np.ndarray):
+            _instances = [
+                self.instances[ind] if ind.shape[0] > 0 else np.asarray([], dtype=int)
+                for ind in _indexes
+            ]
+        elif isinstance(self.instances, sp.csr_matrix):
+            _instances = [
+                self.instances[ind]
+                if ind.shape[0] > 0
+                else sp.csr_matrix(np.empty((0, 0), dtype=int))
+                for ind in _indexes
+            ]
+        _labels = [
+            np.asarray(
+                [
+                    ExClassManager.get_true(_ncl, lbl)
+                    for lbl in (self.labels[ind] if len(ind) > 0 else [])
+                ],
+                dtype=int,
             )
             for ind in _indexes
         ]
+        return [
+            ExtendedCollection(inst, lbl, classes=range(0, _ncl))
+            for (inst, lbl) in zip(_instances, _labels)
+        ]
 
     @classmethod
-    def split_index_by_pred(
-        cls, n_classes: int, instances: np.ndarray
+    def split_inst_by_pred(
+        cls, n_classes: int, instances: np.ndarray | sp.csr_matrix
+    ) -> (List[np.ndarray | sp.csr_matrix], List[float]):
+        _indexes = cls._split_index_by_pred(n_classes, instances)
+        if isinstance(instances, np.ndarray):
+            _instances = [
+                instances[ind] if ind.shape[0] > 0 else np.asarray([], dtype=int)
+                for ind in _indexes
+            ]
+        elif isinstance(instances, sp.csr_matrix):
+            _instances = [
+                instances[ind]
+                if ind.shape[0] > 0
+                else sp.csr_matrix(np.empty((0, 0), dtype=int))
+                for ind in _indexes
+            ]
+        norms = [inst.shape[0] / instances.shape[0] for inst in _instances]
+        return _instances, norms
+
+    @classmethod
+    def _split_index_by_pred(
+        cls, n_classes: int, instances: np.ndarray | sp.csr_matrix
     ) -> List[np.ndarray]:
-        _pred_label = [np.argmax(inst[-n_classes:], axis=0) for inst in instances]
+        if isinstance(instances, np.ndarray):
+            _pred_label = [np.argmax(inst[-n_classes:], axis=0) for inst in instances]
+        elif isinstance(instances, sp.csr_matrix):
+            _pred_label = [
+                np.argmax(inst[:, -n_classes:].toarray().flatten(), axis=0)
+                for inst in instances
+            ]
+        else:
+            raise ValueError("Unsupported matrix format")
+
         return [
-            np.asarray([j for (j, x) in enumerate(_pred_label) if x == i])
+            np.asarray([j for (j, x) in enumerate(_pred_label) if x == i], dtype=int)
             for i in range(0, n_classes)
         ]
 
     @classmethod
     def extend_instances(
-        cls, instances: np.ndarray, pred_proba: np.ndarray
-    ) -> np.ndarray:
+        cls, instances: np.ndarray | sp.csr_matrix, pred_proba: np.ndarray
+    ) -> np.ndarray | sp.csr_matrix:
         if isinstance(instances, sp.csr_matrix):
             _pred_proba = sp.csr_matrix(pred_proba)
             n_x = sp.hstack([instances, _pred_proba])
diff --git a/quacc/error.py b/quacc/error.py
index e8d315a..90e5701 100644
--- a/quacc/error.py
+++ b/quacc/error.py
@@ -3,13 +3,12 @@ import quapy as qp
 def from_name(err_name):
     if err_name == 'f1e':
         return f1e
+    elif err_name == 'f1':
+        return f1
     else:
         return qp.error.from_name(err_name)
     
-def f1e(prev):
-    return 1 - f1_score(prev)
-
-def f1_score(prev):
+def f1(prev):
     # https://github.com/dice-group/gerbil/wiki/Precision,-Recall-and-F1-measure
     if prev[0] == 0 and prev[1] == 0 and prev[2] == 0:
         return 1.0
@@ -21,3 +20,6 @@ def f1_score(prev):
         recall = prev[0] / (prev[0] + prev[1])
         precision = prev[0] / (prev[0] + prev[2]) 
         return 2 * (precision * recall) / (precision + recall)
+
+def f1e(prev):
+    return 1 - f1(prev)
diff --git a/quacc/estimator.py b/quacc/estimator.py
index 5152b36..2fccfe1 100644
--- a/quacc/estimator.py
+++ b/quacc/estimator.py
@@ -11,17 +11,10 @@ from sklearn.model_selection import cross_val_predict
 from quacc.data import ExtendedCollection as EC
 
 
-def _check_prevalence_classes(true_classes, estim_classes, estim_prev):
-    for _cls in true_classes:
-        if _cls not in estim_classes:
-            estim_prev = np.insert(estim_prev, _cls, [0.0], axis=0)
-    return estim_prev
-
-
 class AccuracyEstimator:
     def extend(self, base: LabelledCollection, pred_proba=None) -> EC:
         if not pred_proba:
-            pred_proba = self.model.predict_proba(base.X)
+            pred_proba = self.c_model.predict_proba(base.X)
         return EC.extend_collection(base, pred_proba)
 
     @abstractmethod
@@ -62,10 +55,16 @@ class MulticlassAccuracyEstimator(AccuracyEstimator):
 
         estim_prev = self.q_model.quantify(e_inst)
 
-        return _check_prevalence_classes(
+        return self._check_prevalence_classes(
             self.e_train.classes_, self.q_model.classes_, estim_prev
         )
 
+    def _check_prevalence_classes(self, true_classes, estim_classes, estim_prev):
+        for _cls in true_classes:
+            if _cls not in estim_classes:
+                estim_prev = np.insert(estim_prev, _cls, [0.0], axis=0)
+        return estim_prev
+
 
 class BinaryQuantifierAccuracyEstimator(AccuracyEstimator):
     def __init__(self, c_model: BaseEstimator):
@@ -86,10 +85,11 @@ class BinaryQuantifierAccuracyEstimator(AccuracyEstimator):
         else:
             self.e_train = train
 
+        self.n_classes = self.e_train.n_classes
         [e_train_0, e_train_1] = self.e_train.split_by_pred()
 
-        self.q_model_0.fit(self.e_train_0)
-        self.q_model_1.fit(self.e_train_1)
+        self.q_model_0.fit(e_train_0)
+        self.q_model_1.fit(e_train_1)
 
     def estimate(self, instances, ext=False):
         # TODO: test
@@ -99,17 +99,24 @@ class BinaryQuantifierAccuracyEstimator(AccuracyEstimator):
         else:
             e_inst = instances
 
-        _ncl = int(math.sqrt(self.e_train.n_classes))
-        [e_inst_0, e_inst_1] = [
-            e_inst[ind] for ind in EC.split_index_by_pred(_ncl, e_inst)
+        _ncl = int(math.sqrt(self.n_classes))
+        s_inst, norms = EC.split_inst_by_pred(_ncl, e_inst)
+        [estim_prev_0, estim_prev_1] = [
+            self._quantify_helper(inst, norm, q_model)
+            for (inst, norm, q_model) in zip(
+                s_inst, norms, [self.q_model_0, self.q_model_1]
+            )
         ]
-        estim_prev_0 = self.q_model_0.quantify(e_inst_0)
-        estim_prev_1 = self.q_model_1.quantify(e_inst_1)
 
         estim_prev = []
         for prev_row in zip(estim_prev_0, estim_prev_1):
             for prev in prev_row:
                 estim_prev.append(prev)
 
-        return estim_prev
+        return np.asarray(estim_prev)
 
+    def _quantify_helper(self, inst, norm, q_model):
+        if inst.shape[0] > 0:
+            return np.asarray(list(map(lambda p: p * norm, q_model.quantify(inst))))
+        else:
+            return np.asarray([0.0, 0.0])
diff --git a/quacc/evaluation.py b/quacc/evaluation.py
index cbce5af..48502d4 100644
--- a/quacc/evaluation.py
+++ b/quacc/evaluation.py
@@ -104,7 +104,7 @@ def evaluation_report(
     base_prevs, true_prevs, estim_prevs = estimate(estimator, protocol)
 
     if error_metrics == "all":
-        error_metrics = ["mae", "rae", "mrae", "kld", "nkld", "f1e"]
+        error_metrics = ["ae", "f1"]
 
     error_funcs = [
         error.from_name(e) if isinstance(e, str) else e for e in error_metrics
@@ -112,6 +112,9 @@ def evaluation_report(
     assert all(hasattr(e, "__call__") for e in error_funcs), "invalid error function"
     error_names = [e.__name__ for e in error_funcs]
     error_cols = error_names.copy()
+    if "f1" in error_cols:
+        error_cols.remove("f1")
+        error_cols.extend(["f1_true", "f1_estim", "f1_dist"])
     if "f1e" in error_cols:
         error_cols.remove("f1e")
         error_cols.extend(["f1e_true", "f1e_estim"])
@@ -136,6 +139,12 @@ def evaluation_report(
                 series[("errors", "f1e_true")] = error_metric(true_prev)
                 series[("errors", "f1e_estim")] = error_metric(estim_prev)
                 continue
+            if error_name == "f1":
+                f1_true, f1_estim = error_metric(true_prev), error_metric(estim_prev)
+                series[("errors", "f1_true")] = f1_true
+                series[("errors", "f1_estim")] = f1_estim
+                series[("errors", "f1_dist")] = abs(f1_estim - f1_true)
+                continue
 
             score = error_metric(true_prev, estim_prev)
             series[("errors", error_name)] = score
diff --git a/quacc/main.py b/quacc/main.py
index b251549..f8e14aa 100644
--- a/quacc/main.py
+++ b/quacc/main.py
@@ -4,7 +4,10 @@ from quapy.protocol import APP
 from sklearn.linear_model import LogisticRegression
 
 import quacc.evaluation as eval
-from quacc.estimator import MulticlassAccuracyEstimator
+from quacc.estimator import (
+    BinaryQuantifierAccuracyEstimator,
+    MulticlassAccuracyEstimator,
+)
 
 from quacc.data import get_dataset
 
@@ -12,8 +15,11 @@ qp.environ["SAMPLE_SIZE"] = 100
 
 pd.set_option("display.float_format", "{:.4f}".format)
 
+dataset_name = "imdb"
 
-def test_2(dataset_name):
+
+def estimate_multiclass():
+    print(dataset_name)
     train, test = get_dataset(dataset_name)
 
     model = LogisticRegression()
@@ -45,19 +51,52 @@ def test_2(dataset_name):
         protocol,
         aggregate=True,
     )
+    # print(df.to_latex())
     print(df.to_string())
+    # print(df.to_html())
+    print()
 
 
-def main():
-    for dataset_name in [
-        "imdb",
-        # "hp",
-        # "spambase",
-    ]:
-        print(dataset_name)
-        test_2(dataset_name)
-        print("*" * 50)
+def estimate_binary():
+    print(dataset_name)
+    train, test = get_dataset(dataset_name)
+
+    model = LogisticRegression()
+
+    print(f"fitting model {model.__class__.__name__}...", end=" ", flush=True)
+    model.fit(*train.Xy)
+    print("fit")
+
+    estimator = BinaryQuantifierAccuracyEstimator(model)
+
+    print(
+        f"fitting qmodel {estimator.q_model_0.__class__.__name__}...",
+        end=" ",
+        flush=True,
+    )
+    estimator.fit(train)
+    print("fit")
+
+    n_prevalences = 21
+    repreats = 1000
+    protocol = APP(test, n_prevalences=n_prevalences, repeats=repreats)
+    print(
+        f"Tests:\n\
+        protocol={protocol.__class__.__name__}\n\
+        n_prevalences={n_prevalences}\n\
+        repreats={repreats}\n\
+        executing...\n"
+    )
+    df = eval.evaluation_report(
+        estimator,
+        protocol,
+        aggregate=True,
+    )
+    # print(df.to_latex(float_format="{:.4f}".format))
+    print(df.to_string())
+    # print(df.to_html())
+    print()
 
 
 if __name__ == "__main__":
-    main()
+    estimate_multiclass()
diff --git a/tests/test_data.py b/tests/test_data.py
index 8bc8c0f..d8e6b3c 100644
--- a/tests/test_data.py
+++ b/tests/test_data.py
@@ -1,6 +1,7 @@
 import pytest
 from quacc.data import ExClassManager as ECM, ExtendedCollection
 import numpy as np
+import scipy.sparse as sp
 
 
 class TestExClassManager:
@@ -45,50 +46,180 @@ class TestExClassManager:
 
 
 class TestExtendedCollection:
+    @pytest.mark.parametrize(
+        "instances,result",
+        [
+            (
+                np.asarray(
+                    [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]]
+                ),
+                [np.asarray([1, 3]), np.asarray([0, 2])],
+            ),
+            (
+                sp.csr_matrix(
+                    [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]]
+                ),
+                [np.asarray([1, 3]), np.asarray([0, 2])],
+            ),
+            (
+                np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
+                [np.asarray([], dtype=int), np.asarray([0, 1])],
+            ),
+            (
+                sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
+                [np.asarray([], dtype=int), np.asarray([0, 1])],
+            ),
+            (
+                np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
+                [np.asarray([0, 1]), np.asarray([], dtype=int)],
+            ),
+            (
+                sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
+                [np.asarray([0, 1]), np.asarray([], dtype=int)],
+            ),
+        ],
+    )
+    def test__split_index_by_pred(self, instances, result):
+        ncl = 2
+        assert all(
+            np.array_equal(a, b)
+            for (a, b) in zip(
+                ExtendedCollection._split_index_by_pred(ncl, instances),
+                result,
+            )
+        )
+
+    @pytest.mark.parametrize(
+        "instances,s_inst,norms",
+        [
+            (
+                np.asarray(
+                    [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]]
+                ),
+                [
+                    np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
+                    np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
+                ],
+                [0.5, 0.5],
+            ),
+            (
+                sp.csr_matrix(
+                    [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]]
+                ),
+                [
+                    sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
+                    sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
+                ],
+                [0.5, 0.5],
+            ),
+            (
+                np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
+                [
+                    np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
+                    np.asarray([], dtype=int),
+                ],
+                [1.0, 0.0],
+            ),
+            (
+                sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
+                [
+                    sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
+                    sp.csr_matrix([], dtype=int),
+                ],
+                [1.0, 0.0],
+            ),
+            (
+                np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
+                [
+                    np.asarray([], dtype=int),
+                    np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
+                ],
+                [0.0, 1.0],
+            ),
+            (
+                sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
+                [
+                    sp.csr_matrix([], dtype=int),
+                    sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
+                ],
+                [0.0, 1.0],
+            ),
+        ],
+    )
+    def test_split_inst_by_pred(self, instances, s_inst, norms):
+        ncl = 2
+        _s_inst, _norms = ExtendedCollection.split_inst_by_pred(ncl, instances)
+        if isinstance(s_inst, np.ndarray):
+            assert all(np.array_equal(a, b) for (a, b) in zip(_s_inst, s_inst))
+        if isinstance(s_inst, sp.csr_matrix):
+            assert all((a != b).nnz == 0 for (a, b) in zip(_s_inst, s_inst))
+        assert all(a == b for (a, b) in zip(_norms, norms))
+
     @pytest.mark.parametrize(
         "instances,labels,inst0,lbl0,inst1,lbl1",
         [
             (
-                [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]],
-                [3, 0, 1, 2],
-                [[1, 0.54, 0.46], [3, 0.6, 0.4]],
-                [0, 1],
-                [[0, 0.3, 0.7], [2, 0.28, 0.72]],
-                [1, 0],
+                np.asarray(
+                    [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]]
+                ),
+                np.asarray([3, 0, 1, 2]),
+                np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
+                np.asarray([0, 1]),
+                np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
+                np.asarray([1, 0]),
             ),
             (
-                [[0, 0.3, 0.7], [2, 0.28, 0.72]],
-                [3, 1],
-                [],
-                [],
-                [[0, 0.3, 0.7], [2, 0.28, 0.72]],
-                [1, 0],
+                sp.csr_matrix(
+                    [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]]
+                ),
+                np.asarray([3, 0, 1, 2]),
+                sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
+                np.asarray([0, 1]),
+                sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
+                np.asarray([1, 0]),
             ),
             (
-                [[1, 0.54, 0.46], [3, 0.6, 0.4]],
-                [0, 2],
-                [[1, 0.54, 0.46], [3, 0.6, 0.4]],
-                [0, 1],
-                [],
-                [],
+                np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
+                np.asarray([3, 1]),
+                np.asarray([], dtype=int),
+                np.asarray([], dtype=int),
+                np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
+                np.asarray([1, 0]),
+            ),
+            (
+                sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
+                np.asarray([3, 1]),
+                sp.csr_matrix(np.empty((0, 0), dtype=int)),
+                np.asarray([], dtype=int),
+                sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
+                np.asarray([1, 0]),
+            ),
+            (
+                np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
+                np.asarray([0, 2]),
+                np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
+                np.asarray([0, 1]),
+                np.asarray([], dtype=int),
+                np.asarray([], dtype=int),
+            ),
+            (
+                sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
+                np.asarray([0, 2]),
+                sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
+                np.asarray([0, 1]),
+                sp.csr_matrix(np.empty((0, 0), dtype=int)),
+                np.asarray([], dtype=int),
             ),
-
         ],
     )
     def test_split_by_pred(self, instances, labels, inst0, lbl0, inst1, lbl1):
-        ec = ExtendedCollection(
-            np.asarray(instances), np.asarray(labels), classes=range(0, 4)
-        )
+        ec = ExtendedCollection(instances, labels, classes=range(0, 4))
         [ec0, ec1] = ec.split_by_pred()
-        print(ec0.X, np.asarray(inst0))
-        assert( np.array_equal(ec0.X, np.asarray(inst0)) )
-        print(ec0.y, np.asarray(lbl0))
-        assert( np.array_equal(ec0.y, np.asarray(lbl0)) )
-        print(ec1.X, np.asarray(inst1))
-        assert( np.array_equal(ec1.X, np.asarray(inst1)) )
-        print(ec1.y, np.asarray(lbl1))
-        assert( np.array_equal(ec1.y, np.asarray(lbl1)) )
-        
-
-        
-        
+        if isinstance(instances, np.ndarray):
+            assert np.array_equal(ec0.X, inst0)
+            assert np.array_equal(ec1.X, inst1)
+        if isinstance(instances, sp.csr_matrix):
+            assert (ec0.X != inst0).nnz == 0
+            assert (ec1.X != inst1).nnz == 0
+        assert np.array_equal(ec0.y, lbl0)
+        assert np.array_equal(ec1.y, lbl1)
diff --git a/tests/test_estimator.py b/tests/test_estimator.py
index 190b0ba..d13afe2 100644
--- a/tests/test_estimator.py
+++ b/tests/test_estimator.py
@@ -1,4 +1,66 @@
-class TestBinaryQuantifierAccuracyEstimator:
+import pytest
+import numpy as np
+import scipy.sparse as sp
+from sklearn.linear_model import LogisticRegression
 
-    def test_estimate(self):
-        pass
\ No newline at end of file
+from quacc.estimator import BinaryQuantifierAccuracyEstimator
+
+
+class TestBinaryQuantifierAccuracyEstimator:
+    @pytest.mark.parametrize(
+        "instances,preds0,preds1,result",
+        [
+            (
+                np.asarray(
+                    [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]]
+                ),
+                np.asarray([0.3, 0.7]),
+                np.asarray([0.4, 0.6]),
+                np.asarray([0.15, 0.2, 0.35, 0.3]),
+            ),
+            (
+                sp.csr_matrix(
+                    [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]]
+                ),
+                np.asarray([0.3, 0.7]),
+                np.asarray([0.4, 0.6]),
+                np.asarray([0.15, 0.2, 0.35, 0.3]),
+            ),
+            (
+                np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
+                np.asarray([0.3, 0.7]),
+                np.asarray([0.4, 0.6]),
+                np.asarray([0.0, 0.4, 0.0, 0.6]),
+            ),
+            (
+                sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
+                np.asarray([0.3, 0.7]),
+                np.asarray([0.4, 0.6]),
+                np.asarray([0.0, 0.4, 0.0, 0.6]),
+            ),
+            (
+                np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
+                np.asarray([0.3, 0.7]),
+                np.asarray([0.4, 0.6]),
+                np.asarray([0.3, 0.0, 0.7, 0.0]),
+            ),
+            (
+                sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
+                np.asarray([0.3, 0.7]),
+                np.asarray([0.4, 0.6]),
+                np.asarray([0.3, 0.0, 0.7, 0.0]),
+            ),
+        ],
+    )
+    def test_estimate_ndarray(self, mocker, instances, preds0, preds1, result):
+        estimator = BinaryQuantifierAccuracyEstimator(LogisticRegression())
+        estimator.n_classes = 4
+        with mocker.patch.object(estimator.q_model_0, "quantify"), mocker.patch.object(
+            estimator.q_model_1, "quantify"
+        ):
+            estimator.q_model_0.quantify.return_value = preds0
+            estimator.q_model_1.quantify.return_value = preds1
+            assert np.array_equal(
+                estimator.estimate(instances, ext=True),
+                result,
+            )