rcv1_CCAT
> train: [0.09996662 0.90003338]
> validation: [0.09996662 0.90003338]
> evaluate_bin_sld: 198.301s
> evaluate_mul_sld: 53.156s
> kfcv: 41.095s
> atc_mc: 42.167s
> atc_ne: 41.909s
> doc_feat: 35.796s
> tot: 202.108s
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>bin</th>
<th>mul</th>
<th>kfcv</th>
<th>atc_mc</th>
<th>atc_ne</th>
<th>doc_feat</th>
</tr>
</thead>
<tbody>
<tr>
<th>(0.0, 1.0)</th>
<td>0.0048</td>
<td>0.0040</td>
<td>0.0866</td>
<td>0.0243</td>
<td>0.0243</td>
<td>0.0830</td>
</tr>
<tr>
<th>(0.05, 0.95)</th>
<td>0.0060</td>
<td>0.0072</td>
<td>0.0441</td>
<td>0.0134</td>
<td>0.0134</td>
<td>0.0407</td>
</tr>
<tr>
<th>(0.1, 0.9)</th>
<td>0.0084</td>
<td>0.0103</td>
<td>0.0032</td>
<td>0.0070</td>
<td>0.0070</td>
<td>0.0036</td>
</tr>
<tr>
<th>(0.15, 0.85)</th>
<td>0.0127</td>
<td>0.0172</td>
<td>0.0418</td>
<td>0.0090</td>
<td>0.0090</td>
<td>0.0450</td>
</tr>
<tr>
<th>(0.2, 0.8)</th>
<td>0.0184</td>
<td>0.0246</td>
<td>0.0841</td>
<td>0.0168</td>
<td>0.0168</td>
<td>0.0872</td>
</tr>
<tr>
<th>(0.25, 0.75)</th>
<td>0.0231</td>
<td>0.0318</td>
<td>0.1246</td>
<td>0.0239</td>
<td>0.0239</td>
<td>0.1276</td>
</tr>
<tr>
<th>(0.3, 0.7)</th>
<td>0.0313</td>
<td>0.0426</td>
<td>0.1678</td>
<td>0.0334</td>
<td>0.0334</td>
<td>0.1706</td>
</tr>
<tr>
<th>(0.35, 0.65)</th>
<td>0.0392</td>
<td>0.0536</td>
<td>0.2110</td>
<td>0.0422</td>
<td>0.0422</td>
<td>0.2137</td>
</tr>
<tr>
<th>(0.4, 0.6)</th>
<td>0.0418</td>
<td>0.0563</td>
<td>0.2528</td>
<td>0.0541</td>
<td>0.0541</td>
<td>0.2555</td>
</tr>
<tr>
<th>(0.45, 0.55)</th>
<td>0.0527</td>
<td>0.0715</td>
<td>0.2966</td>
<td>0.0622</td>
<td>0.0622</td>
<td>0.2991</td>
</tr>
<tr>
<th>(0.5, 0.5)</th>
<td>0.0569</td>
<td>0.0771</td>
<td>0.3383</td>
<td>0.0749</td>
<td>0.0749</td>
<td>0.3407</td>
</tr>
<tr>
<th>(0.55, 0.45)</th>
<td>0.0637</td>
<td>0.0867</td>
<td>0.3817</td>
<td>0.0847</td>
<td>0.0847</td>
<td>0.3840</td>
</tr>
<tr>
<th>(0.6, 0.4)</th>
<td>0.0727</td>
<td>0.0999</td>
<td>0.4250</td>
<td>0.0943</td>
<td>0.0943</td>
<td>0.4272</td>
</tr>
<tr>
<th>(0.65, 0.35)</th>
<td>0.0778</td>
<td>0.1062</td>
<td>0.4662</td>
<td>0.1040</td>
<td>0.1040</td>
<td>0.4683</td>
</tr>
<tr>
<th>(0.7, 0.3)</th>
<td>0.0825</td>
<td>0.1118</td>
<td>0.5099</td>
<td>0.1131</td>
<td>0.1131</td>
<td>0.5119</td>
</tr>
<tr>
<th>(0.75, 0.25)</th>
<td>0.0879</td>
<td>0.1197</td>
<td>0.5519</td>
<td>0.1217</td>
<td>0.1217</td>
<td>0.5537</td>
</tr>
<tr>
<th>(0.8, 0.2)</th>
<td>0.0887</td>
<td>0.1192</td>
<td>0.5945</td>
<td>0.1334</td>
<td>0.1334</td>
<td>0.5963</td>
</tr>
<tr>
<th>(0.85, 0.15)</th>
<td>0.0926</td>
<td>0.1269</td>
<td>0.6368</td>
<td>0.1426</td>
<td>0.1426</td>
<td>0.6384</td>
</tr>
<tr>
<th>(0.9, 0.1)</th>
<td>0.0887</td>
<td>0.1250</td>
<td>0.6791</td>
<td>0.1528</td>
<td>0.1528</td>
<td>0.6806</td>
</tr>
<tr>
<th>(0.95, 0.05)</th>
<td>0.0501</td>
<td>0.0961</td>
<td>0.7227</td>
<td>0.1614</td>
<td>0.1614</td>
<td>0.7241</td>
</tr>
<tr>
<th>(1.0, 0.0)</th>
<td>0.0004</td>
<td>0.0358</td>
<td>0.7631</td>
<td>0.1704</td>
<td>0.1704</td>
<td>0.7643</td>
</tr>
</tbody>
</table>
> train: [0.19993324 0.80006676]
> validation: [0.20010013 0.79989987]
> evaluate_bin_sld: 199.250s
> evaluate_mul_sld: 55.414s
> kfcv: 41.131s
> atc_mc: 42.125s
> atc_ne: 41.892s
> doc_feat: 35.279s
> tot: 202.707s
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>bin</th>
<th>mul</th>
<th>kfcv</th>
<th>atc_mc</th>
<th>atc_ne</th>
<th>doc_feat</th>
</tr>
</thead>
<tbody>
<tr>
<th>(0.0, 1.0)</th>
<td>0.0055</td>
<td>0.0058</td>
<td>0.0915</td>
<td>0.0147</td>
<td>0.0147</td>
<td>0.0775</td>
</tr>
<tr>
<th>(0.05, 0.95)</th>
<td>0.0157</td>
<td>0.0084</td>
<td>0.0719</td>
<td>0.0130</td>
<td>0.0130</td>
<td>0.0581</td>
</tr>
<tr>
<th>(0.1, 0.9)</th>
<td>0.0154</td>
<td>0.0099</td>
<td>0.0503</td>
<td>0.0108</td>
<td>0.0108</td>
<td>0.0365</td>
</tr>
<tr>
<th>(0.15, 0.85)</th>
<td>0.0141</td>
<td>0.0111</td>
<td>0.0292</td>
<td>0.0104</td>
<td>0.0104</td>
<td>0.0158</td>
</tr>
<tr>
<th>(0.2, 0.8)</th>
<td>0.0120</td>
<td>0.0116</td>
<td>0.0103</td>
<td>0.0100</td>
<td>0.0100</td>
<td>0.0068</td>
</tr>
<tr>
<th>(0.25, 0.75)</th>
<td>0.0098</td>
<td>0.0124</td>
<td>0.0115</td>
<td>0.0091</td>
<td>0.0091</td>
<td>0.0243</td>
</tr>
<tr>
<th>(0.3, 0.7)</th>
<td>0.0079</td>
<td>0.0131</td>
<td>0.0312</td>
<td>0.0106</td>
<td>0.0106</td>
<td>0.0445</td>
</tr>
<tr>
<th>(0.35, 0.65)</th>
<td>0.0087</td>
<td>0.0154</td>
<td>0.0529</td>
<td>0.0097</td>
<td>0.0097</td>
<td>0.0660</td>
</tr>
<tr>
<th>(0.4, 0.6)</th>
<td>0.0074</td>
<td>0.0143</td>
<td>0.0729</td>
<td>0.0110</td>
<td>0.0110</td>
<td>0.0859</td>
</tr>
<tr>
<th>(0.45, 0.55)</th>
<td>0.0082</td>
<td>0.0148</td>
<td>0.0933</td>
<td>0.0111</td>
<td>0.0111</td>
<td>0.1062</td>
</tr>
<tr>
<th>(0.5, 0.5)</th>
<td>0.0081</td>
<td>0.0152</td>
<td>0.1152</td>
<td>0.0136</td>
<td>0.0136</td>
<td>0.1280</td>
</tr>
<tr>
<th>(0.55, 0.45)</th>
<td>0.0104</td>
<td>0.0164</td>
<td>0.1384</td>
<td>0.0147</td>
<td>0.0147</td>
<td>0.1511</td>
</tr>
<tr>
<th>(0.6, 0.4)</th>
<td>0.0108</td>
<td>0.0193</td>
<td>0.1567</td>
<td>0.0168</td>
<td>0.0168</td>
<td>0.1692</td>
</tr>
<tr>
<th>(0.65, 0.35)</th>
<td>0.0129</td>
<td>0.0212</td>
<td>0.1806</td>
<td>0.0196</td>
<td>0.0196</td>
<td>0.1930</td>
</tr>
<tr>
<th>(0.7, 0.3)</th>
<td>0.0134</td>
<td>0.0242</td>
<td>0.2005</td>
<td>0.0178</td>
<td>0.0178</td>
<td>0.2128</td>
</tr>
<tr>
<th>(0.75, 0.25)</th>
<td>0.0162</td>
<td>0.0238</td>
<td>0.2196</td>
<td>0.0201</td>
<td>0.0201</td>
<td>0.2318</td>
</tr>
<tr>
<th>(0.8, 0.2)</th>
<td>0.0161</td>
<td>0.0248</td>
<td>0.2425</td>
<td>0.0214</td>
<td>0.0214</td>
<td>0.2546</td>
</tr>
<tr>
<th>(0.85, 0.15)</th>
<td>0.0207</td>
<td>0.0320</td>
<td>0.2620</td>
<td>0.0227</td>
<td>0.0227</td>
<td>0.2740</td>
</tr>
<tr>
<th>(0.9, 0.1)</th>
<td>0.0233</td>
<td>0.0340</td>
<td>0.2841</td>
<td>0.0267</td>
<td>0.0267</td>
<td>0.2960</td>
</tr>
<tr>
<th>(0.95, 0.05)</th>
<td>0.0261</td>
<td>0.0393</td>
<td>0.3034</td>
<td>0.0274</td>
<td>0.0274</td>
<td>0.3151</td>
</tr>
<tr>
<th>(1.0, 0.0)</th>
<td>0.0019</td>
<td>0.0162</td>
<td>0.3217</td>
<td>0.0311</td>
<td>0.0311</td>
<td>0.3333</td>
</tr>
</tbody>
</table>
> train: [0.29989987 0.70010013]
> validation: [0.30006676 0.69993324]
> evaluate_bin_sld: 197.848s
> evaluate_mul_sld: 55.610s
> kfcv: 40.783s
> atc_mc: 42.124s
> atc_ne: 41.370s
> doc_feat: 35.340s
> tot: 199.287s
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>bin</th>
<th>mul</th>
<th>kfcv</th>
<th>atc_mc</th>
<th>atc_ne</th>
<th>doc_feat</th>
</tr>
</thead>
<tbody>
<tr>
<th>(0.0, 1.0)</th>
<td>0.0051</td>
<td>0.0059</td>
<td>0.0530</td>
<td>0.0059</td>
<td>0.0059</td>
<td>0.0422</td>
</tr>
<tr>
<th>(0.05, 0.95)</th>
<td>0.0108</td>
<td>0.0082</td>
<td>0.0455</td>
<td>0.0063</td>
<td>0.0063</td>
<td>0.0347</td>
</tr>
<tr>
<th>(0.1, 0.9)</th>
<td>0.0127</td>
<td>0.0110</td>
<td>0.0356</td>
<td>0.0062</td>
<td>0.0062</td>
<td>0.0250</td>
</tr>
<tr>
<th>(0.15, 0.85)</th>
<td>0.0147</td>
<td>0.0145</td>
<td>0.0265</td>
<td>0.0076</td>
<td>0.0076</td>
<td>0.0160</td>
</tr>
<tr>
<th>(0.2, 0.8)</th>
<td>0.0158</td>
<td>0.0162</td>
<td>0.0173</td>
<td>0.0071</td>
<td>0.0071</td>
<td>0.0086</td>
</tr>
<tr>
<th>(0.25, 0.75)</th>
<td>0.0147</td>
<td>0.0158</td>
<td>0.0091</td>
<td>0.0070</td>
<td>0.0070</td>
<td>0.0075</td>
</tr>
<tr>
<th>(0.3, 0.7)</th>
<td>0.0134</td>
<td>0.0162</td>
<td>0.0073</td>
<td>0.0080</td>
<td>0.0080</td>
<td>0.0127</td>
</tr>
<tr>
<th>(0.35, 0.65)</th>
<td>0.0138</td>
<td>0.0178</td>
<td>0.0132</td>
<td>0.0100</td>
<td>0.0100</td>
<td>0.0230</td>
</tr>
<tr>
<th>(0.4, 0.6)</th>
<td>0.0130</td>
<td>0.0180</td>
<td>0.0204</td>
<td>0.0096</td>
<td>0.0096</td>
<td>0.0306</td>
</tr>
<tr>
<th>(0.45, 0.55)</th>
<td>0.0102</td>
<td>0.0149</td>
<td>0.0297</td>
<td>0.0102</td>
<td>0.0102</td>
<td>0.0397</td>
</tr>
<tr>
<th>(0.5, 0.5)</th>
<td>0.0094</td>
<td>0.0160</td>
<td>0.0405</td>
<td>0.0111</td>
<td>0.0111</td>
<td>0.0504</td>
</tr>
<tr>
<th>(0.55, 0.45)</th>
<td>0.0095</td>
<td>0.0135</td>
<td>0.0516</td>
<td>0.0123</td>
<td>0.0123</td>
<td>0.0615</td>
</tr>
<tr>
<th>(0.6, 0.4)</th>
<td>0.0086</td>
<td>0.0132</td>
<td>0.0596</td>
<td>0.0122</td>
<td>0.0122</td>
<td>0.0693</td>
</tr>
<tr>
<th>(0.65, 0.35)</th>
<td>0.0102</td>
<td>0.0123</td>
<td>0.0717</td>
<td>0.0149</td>
<td>0.0149</td>
<td>0.0814</td>
</tr>
<tr>
<th>(0.7, 0.3)</th>
<td>0.0098</td>
<td>0.0115</td>
<td>0.0797</td>
<td>0.0160</td>
<td>0.0160</td>
<td>0.0894</td>
</tr>
<tr>
<th>(0.75, 0.25)</th>
<td>0.0111</td>
<td>0.0108</td>
<td>0.0880</td>
<td>0.0160</td>
<td>0.0160</td>
<td>0.0975</td>
</tr>
<tr>
<th>(0.8, 0.2)</th>
<td>0.0112</td>
<td>0.0093</td>
<td>0.0996</td>
<td>0.0206</td>
<td>0.0206</td>
<td>0.1091</td>
</tr>
<tr>
<th>(0.85, 0.15)</th>
<td>0.0149</td>
<td>0.0119</td>
<td>0.1094</td>
<td>0.0197</td>
<td>0.0197</td>
<td>0.1187</td>
</tr>
<tr>
<th>(0.9, 0.1)</th>
<td>0.0167</td>
<td>0.0137</td>
<td>0.1178</td>
<td>0.0216</td>
<td>0.0216</td>
<td>0.1271</td>
</tr>
<tr>
<th>(0.95, 0.05)</th>
<td>0.0184</td>
<td>0.0145</td>
<td>0.1275</td>
<td>0.0222</td>
<td>0.0222</td>
<td>0.1367</td>
</tr>
<tr>
<th>(1.0, 0.0)</th>
<td>0.0007</td>
<td>0.0099</td>
<td>0.1371</td>
<td>0.0238</td>
<td>0.0238</td>
<td>0.1462</td>
</tr>
</tbody>
</table>
> train: [0.40003338 0.59996662]
> validation: [0.40003338 0.59996662]
> evaluate_bin_sld: 197.597s
> evaluate_mul_sld: 55.556s
> kfcv: 40.650s
> atc_mc: 41.687s
> atc_ne: 41.375s
> doc_feat: 34.998s
> tot: 198.892s
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>bin</th>
<th>mul</th>
<th>kfcv</th>
<th>atc_mc</th>
<th>atc_ne</th>
<th>doc_feat</th>
</tr>
</thead>
<tbody>
<tr>
<th>(0.0, 1.0)</th>
<td>0.0013</td>
<td>0.0048</td>
<td>0.0194</td>
<td>0.0071</td>
<td>0.0071</td>
<td>0.0126</td>
</tr>
<tr>
<th>(0.05, 0.95)</th>
<td>0.0076</td>
<td>0.0084</td>
<td>0.0184</td>
<td>0.0071</td>
<td>0.0071</td>
<td>0.0111</td>
</tr>
<tr>
<th>(0.1, 0.9)</th>
<td>0.0092</td>
<td>0.0107</td>
<td>0.0161</td>
<td>0.0078</td>
<td>0.0078</td>
<td>0.0093</td>
</tr>
<tr>
<th>(0.15, 0.85)</th>
<td>0.0127</td>
<td>0.0149</td>
<td>0.0134</td>
<td>0.0070</td>
<td>0.0070</td>
<td>0.0077</td>
</tr>
<tr>
<th>(0.2, 0.8)</th>
<td>0.0183</td>
<td>0.0200</td>
<td>0.0110</td>
<td>0.0066</td>
<td>0.0066</td>
<td>0.0075</td>
</tr>
<tr>
<th>(0.25, 0.75)</th>
<td>0.0208</td>
<td>0.0230</td>
<td>0.0090</td>
<td>0.0075</td>
<td>0.0075</td>
<td>0.0069</td>
</tr>
<tr>
<th>(0.3, 0.7)</th>
<td>0.0235</td>
<td>0.0260</td>
<td>0.0080</td>
<td>0.0076</td>
<td>0.0076</td>
<td>0.0073</td>
</tr>
<tr>
<th>(0.35, 0.65)</th>
<td>0.0273</td>
<td>0.0306</td>
<td>0.0065</td>
<td>0.0079</td>
<td>0.0079</td>
<td>0.0095</td>
</tr>
<tr>
<th>(0.4, 0.6)</th>
<td>0.0296</td>
<td>0.0335</td>
<td>0.0074</td>
<td>0.0072</td>
<td>0.0072</td>
<td>0.0099</td>
</tr>
<tr>
<th>(0.45, 0.55)</th>
<td>0.0283</td>
<td>0.0313</td>
<td>0.0080</td>
<td>0.0085</td>
<td>0.0085</td>
<td>0.0116</td>
</tr>
<tr>
<th>(0.5, 0.5)</th>
<td>0.0267</td>
<td>0.0317</td>
<td>0.0087</td>
<td>0.0085</td>
<td>0.0085</td>
<td>0.0147</td>
</tr>
<tr>
<th>(0.55, 0.45)</th>
<td>0.0273</td>
<td>0.0331</td>
<td>0.0131</td>
<td>0.0086</td>
<td>0.0086</td>
<td>0.0196</td>
</tr>
<tr>
<th>(0.6, 0.4)</th>
<td>0.0239</td>
<td>0.0320</td>
<td>0.0136</td>
<td>0.0082</td>
<td>0.0082</td>
<td>0.0202</td>
</tr>
<tr>
<th>(0.65, 0.35)</th>
<td>0.0208</td>
<td>0.0290</td>
<td>0.0171</td>
<td>0.0084</td>
<td>0.0084</td>
<td>0.0241</td>
</tr>
<tr>
<th>(0.7, 0.3)</th>
<td>0.0186</td>
<td>0.0288</td>
<td>0.0213</td>
<td>0.0084</td>
<td>0.0084</td>
<td>0.0281</td>
</tr>
<tr>
<th>(0.75, 0.25)</th>
<td>0.0158</td>
<td>0.0261</td>
<td>0.0219</td>
<td>0.0090</td>
<td>0.0090</td>
<td>0.0288</td>
</tr>
<tr>
<th>(0.8, 0.2)</th>
<td>0.0130</td>
<td>0.0235</td>
<td>0.0269</td>
<td>0.0089</td>
<td>0.0089</td>
<td>0.0338</td>
</tr>
<tr>
<th>(0.85, 0.15)</th>
<td>0.0084</td>
<td>0.0180</td>
<td>0.0284</td>
<td>0.0083</td>
<td>0.0083</td>
<td>0.0352</td>
</tr>
<tr>
<th>(0.9, 0.1)</th>
<td>0.0057</td>
<td>0.0134</td>
<td>0.0322</td>
<td>0.0092</td>
<td>0.0092</td>
<td>0.0390</td>
</tr>
<tr>
<th>(0.95, 0.05)</th>
<td>0.0050</td>
<td>0.0091</td>
<td>0.0339</td>
<td>0.0101</td>
<td>0.0101</td>
<td>0.0406</td>
</tr>
<tr>
<th>(1.0, 0.0)</th>
<td>0.0007</td>
<td>0.0064</td>
<td>0.0379</td>
<td>0.0106</td>
<td>0.0106</td>
<td>0.0447</td>
</tr>
</tbody>
</table>
> train: [0.5 0.5]
> validation: [0.5 0.5]
> evaluate_bin_sld: 197.283s
> evaluate_mul_sld: 54.736s
> kfcv: 40.375s
> atc_mc: 41.898s
> atc_ne: 41.366s
> doc_feat: 35.145s
> tot: 198.630s
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>bin</th>
<th>mul</th>
<th>kfcv</th>
<th>atc_mc</th>
<th>atc_ne</th>
<th>doc_feat</th>
</tr>
</thead>
<tbody>
<tr>
<th>(0.0, 1.0)</th>
<td>0.0004</td>
<td>0.0035</td>
<td>0.0257</td>
<td>0.0289</td>
<td>0.0289</td>
<td>0.0344</td>
</tr>
<tr>
<th>(0.05, 0.95)</th>
<td>0.0075</td>
<td>0.0085</td>
<td>0.0224</td>
<td>0.0253</td>
<td>0.0253</td>
<td>0.0310</td>
</tr>
<tr>
<th>(0.1, 0.9)</th>
<td>0.0081</td>
<td>0.0122</td>
<td>0.0205</td>
<td>0.0239</td>
<td>0.0239</td>
<td>0.0292</td>
</tr>
<tr>
<th>(0.15, 0.85)</th>
<td>0.0102</td>
<td>0.0148</td>
<td>0.0180</td>
<td>0.0205</td>
<td>0.0205</td>
<td>0.0267</td>
</tr>
<tr>
<th>(0.2, 0.8)</th>
<td>0.0139</td>
<td>0.0198</td>
<td>0.0165</td>
<td>0.0211</td>
<td>0.0211</td>
<td>0.0248</td>
</tr>
<tr>
<th>(0.25, 0.75)</th>
<td>0.0194</td>
<td>0.0245</td>
<td>0.0141</td>
<td>0.0170</td>
<td>0.0170</td>
<td>0.0224</td>
</tr>
<tr>
<th>(0.3, 0.7)</th>
<td>0.0230</td>
<td>0.0287</td>
<td>0.0137</td>
<td>0.0164</td>
<td>0.0164</td>
<td>0.0222</td>
</tr>
<tr>
<th>(0.35, 0.65)</th>
<td>0.0309</td>
<td>0.0338</td>
<td>0.0132</td>
<td>0.0168</td>
<td>0.0168</td>
<td>0.0210</td>
</tr>
<tr>
<th>(0.4, 0.6)</th>
<td>0.0350</td>
<td>0.0371</td>
<td>0.0097</td>
<td>0.0144</td>
<td>0.0144</td>
<td>0.0164</td>
</tr>
<tr>
<th>(0.45, 0.55)</th>
<td>0.0358</td>
<td>0.0390</td>
<td>0.0086</td>
<td>0.0125</td>
<td>0.0125</td>
<td>0.0150</td>
</tr>
<tr>
<th>(0.5, 0.5)</th>
<td>0.0369</td>
<td>0.0386</td>
<td>0.0073</td>
<td>0.0122</td>
<td>0.0122</td>
<td>0.0138</td>
</tr>
<tr>
<th>(0.55, 0.45)</th>
<td>0.0373</td>
<td>0.0398</td>
<td>0.0071</td>
<td>0.0110</td>
<td>0.0110</td>
<td>0.0128</td>
</tr>
<tr>
<th>(0.6, 0.4)</th>
<td>0.0368</td>
<td>0.0398</td>
<td>0.0064</td>
<td>0.0085</td>
<td>0.0085</td>
<td>0.0103</td>
</tr>
<tr>
<th>(0.65, 0.35)</th>
<td>0.0357</td>
<td>0.0385</td>
<td>0.0074</td>
<td>0.0103</td>
<td>0.0103</td>
<td>0.0105</td>
</tr>
<tr>
<th>(0.7, 0.3)</th>
<td>0.0319</td>
<td>0.0370</td>
<td>0.0067</td>
<td>0.0082</td>
<td>0.0082</td>
<td>0.0086</td>
</tr>
<tr>
<th>(0.75, 0.25)</th>
<td>0.0298</td>
<td>0.0358</td>
<td>0.0079</td>
<td>0.0066</td>
<td>0.0066</td>
<td>0.0070</td>
</tr>
<tr>
<th>(0.8, 0.2)</th>
<td>0.0235</td>
<td>0.0302</td>
<td>0.0073</td>
<td>0.0083</td>
<td>0.0083</td>
<td>0.0069</td>
</tr>
<tr>
<th>(0.85, 0.15)</th>
<td>0.0154</td>
<td>0.0244</td>
<td>0.0097</td>
<td>0.0077</td>
<td>0.0077</td>
<td>0.0066</td>
</tr>
<tr>
<th>(0.9, 0.1)</th>
<td>0.0083</td>
<td>0.0157</td>
<td>0.0108</td>
<td>0.0082</td>
<td>0.0082</td>
<td>0.0069</td>
</tr>
<tr>
<th>(0.95, 0.05)</th>
<td>0.0055</td>
<td>0.0098</td>
<td>0.0131</td>
<td>0.0080</td>
<td>0.0080</td>
<td>0.0066</td>
</tr>
<tr>
<th>(1.0, 0.0)</th>
<td>0.0007</td>
<td>0.0046</td>
<td>0.0145</td>
<td>0.0088</td>
<td>0.0088</td>
<td>0.0082</td>
</tr>
</tbody>
</table>
> train: [0.59996662 0.40003338]
> validation: [0.59996662 0.40003338]
> evaluate_bin_sld: 194.960s
> evaluate_mul_sld: 53.330s
> kfcv: 40.320s
> atc_mc: 41.904s
> atc_ne: 41.423s
> doc_feat: 35.289s
> tot: 196.151s
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>bin</th>
<th>mul</th>
<th>kfcv</th>
<th>atc_mc</th>
<th>atc_ne</th>
<th>doc_feat</th>
</tr>
</thead>
<tbody>
<tr>
<th>(0.0, 1.0)</th>
<td>0.0003</td>
<td>0.0055</td>
<td>0.0815</td>
<td>0.0285</td>
<td>0.0285</td>
<td>0.0825</td>
</tr>
<tr>
<th>(0.05, 0.95)</th>
<td>0.0065</td>
<td>0.0127</td>
<td>0.0747</td>
<td>0.0278</td>
<td>0.0278</td>
<td>0.0758</td>
</tr>
<tr>
<th>(0.1, 0.9)</th>
<td>0.0072</td>
<td>0.0172</td>
<td>0.0677</td>
<td>0.0224</td>
<td>0.0224</td>
<td>0.0688</td>
</tr>
<tr>
<th>(0.15, 0.85)</th>
<td>0.0100</td>
<td>0.0257</td>
<td>0.0627</td>
<td>0.0218</td>
<td>0.0218</td>
<td>0.0638</td>
</tr>
<tr>
<th>(0.2, 0.8)</th>
<td>0.0135</td>
<td>0.0308</td>
<td>0.0548</td>
<td>0.0180</td>
<td>0.0180</td>
<td>0.0560</td>
</tr>
<tr>
<th>(0.25, 0.75)</th>
<td>0.0165</td>
<td>0.0338</td>
<td>0.0491</td>
<td>0.0160</td>
<td>0.0160</td>
<td>0.0503</td>
</tr>
<tr>
<th>(0.3, 0.7)</th>
<td>0.0205</td>
<td>0.0409</td>
<td>0.0438</td>
<td>0.0168</td>
<td>0.0168</td>
<td>0.0450</td>
</tr>
<tr>
<th>(0.35, 0.65)</th>
<td>0.0248</td>
<td>0.0459</td>
<td>0.0374</td>
<td>0.0156</td>
<td>0.0156</td>
<td>0.0386</td>
</tr>
<tr>
<th>(0.4, 0.6)</th>
<td>0.0284</td>
<td>0.0491</td>
<td>0.0277</td>
<td>0.0112</td>
<td>0.0112</td>
<td>0.0290</td>
</tr>
<tr>
<th>(0.45, 0.55)</th>
<td>0.0318</td>
<td>0.0515</td>
<td>0.0224</td>
<td>0.0099</td>
<td>0.0099</td>
<td>0.0237</td>
</tr>
<tr>
<th>(0.5, 0.5)</th>
<td>0.0342</td>
<td>0.0516</td>
<td>0.0159</td>
<td>0.0081</td>
<td>0.0081</td>
<td>0.0170</td>
</tr>
<tr>
<th>(0.55, 0.45)</th>
<td>0.0374</td>
<td>0.0519</td>
<td>0.0111</td>
<td>0.0073</td>
<td>0.0073</td>
<td>0.0121</td>
</tr>
<tr>
<th>(0.6, 0.4)</th>
<td>0.0410</td>
<td>0.0537</td>
<td>0.0069</td>
<td>0.0079</td>
<td>0.0079</td>
<td>0.0075</td>
</tr>
<tr>
<th>(0.65, 0.35)</th>
<td>0.0444</td>
<td>0.0517</td>
<td>0.0064</td>
<td>0.0076</td>
<td>0.0076</td>
<td>0.0064</td>
</tr>
<tr>
<th>(0.7, 0.3)</th>
<td>0.0438</td>
<td>0.0502</td>
<td>0.0100</td>
<td>0.0085</td>
<td>0.0085</td>
<td>0.0090</td>
</tr>
<tr>
<th>(0.75, 0.25)</th>
<td>0.0458</td>
<td>0.0483</td>
<td>0.0171</td>
<td>0.0089</td>
<td>0.0089</td>
<td>0.0157</td>
</tr>
<tr>
<th>(0.8, 0.2)</th>
<td>0.0412</td>
<td>0.0419</td>
<td>0.0218</td>
<td>0.0105</td>
<td>0.0105</td>
<td>0.0204</td>
</tr>
<tr>
<th>(0.85, 0.15)</th>
<td>0.0319</td>
<td>0.0348</td>
<td>0.0291</td>
<td>0.0117</td>
<td>0.0117</td>
<td>0.0276</td>
</tr>
<tr>
<th>(0.9, 0.1)</th>
<td>0.0192</td>
<td>0.0254</td>
<td>0.0358</td>
<td>0.0147</td>
<td>0.0147</td>
<td>0.0343</td>
</tr>
<tr>
<th>(0.95, 0.05)</th>
<td>0.0079</td>
<td>0.0154</td>
<td>0.0427</td>
<td>0.0166</td>
<td>0.0166</td>
<td>0.0412</td>
</tr>
<tr>
<th>(1.0, 0.0)</th>
<td>0.0005</td>
<td>0.0034</td>
<td>0.0490</td>
<td>0.0190</td>
<td>0.0190</td>
<td>0.0474</td>
</tr>
</tbody>
</table>
> train: [0.69993324 0.30006676]
> validation: [0.70010013 0.29989987]
> evaluate_bin_sld: 196.856s
> evaluate_mul_sld: 54.245s
> kfcv: 41.167s
> atc_mc: 42.203s
> atc_ne: 41.565s
> doc_feat: 34.998s
> tot: 198.332s
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>bin</th>
<th>mul</th>
<th>kfcv</th>
<th>atc_mc</th>
<th>atc_ne</th>
<th>doc_feat</th>
</tr>
</thead>
<tbody>
<tr>
<th>(0.0, 1.0)</th>
<td>0.0003</td>
<td>0.0071</td>
<td>0.1570</td>
<td>0.0625</td>
<td>0.0625</td>
<td>0.1677</td>
</tr>
<tr>
<th>(0.05, 0.95)</th>
<td>0.0089</td>
<td>0.0102</td>
<td>0.1428</td>
<td>0.0548</td>
<td>0.0548</td>
<td>0.1536</td>
</tr>
<tr>
<th>(0.1, 0.9)</th>
<td>0.0078</td>
<td>0.0121</td>
<td>0.1327</td>
<td>0.0521</td>
<td>0.0521</td>
<td>0.1435</td>
</tr>
<tr>
<th>(0.15, 0.85)</th>
<td>0.0073</td>
<td>0.0155</td>
<td>0.1227</td>
<td>0.0517</td>
<td>0.0517</td>
<td>0.1336</td>
</tr>
<tr>
<th>(0.2, 0.8)</th>
<td>0.0081</td>
<td>0.0196</td>
<td>0.1094</td>
<td>0.0464</td>
<td>0.0464</td>
<td>0.1203</td>
</tr>
<tr>
<th>(0.25, 0.75)</th>
<td>0.0095</td>
<td>0.0225</td>
<td>0.1001</td>
<td>0.0427</td>
<td>0.0427</td>
<td>0.1111</td>
</tr>
<tr>
<th>(0.3, 0.7)</th>
<td>0.0117</td>
<td>0.0272</td>
<td>0.0885</td>
<td>0.0400</td>
<td>0.0400</td>
<td>0.0995</td>
</tr>
<tr>
<th>(0.35, 0.65)</th>
<td>0.0131</td>
<td>0.0309</td>
<td>0.0774</td>
<td>0.0368</td>
<td>0.0368</td>
<td>0.0885</td>
</tr>
<tr>
<th>(0.4, 0.6)</th>
<td>0.0144</td>
<td>0.0333</td>
<td>0.0626</td>
<td>0.0307</td>
<td>0.0307</td>
<td>0.0737</td>
</tr>
<tr>
<th>(0.45, 0.55)</th>
<td>0.0179</td>
<td>0.0365</td>
<td>0.0528</td>
<td>0.0297</td>
<td>0.0297</td>
<td>0.0640</td>
</tr>
<tr>
<th>(0.5, 0.5)</th>
<td>0.0183</td>
<td>0.0359</td>
<td>0.0418</td>
<td>0.0259</td>
<td>0.0259</td>
<td>0.0531</td>
</tr>
<tr>
<th>(0.55, 0.45)</th>
<td>0.0189</td>
<td>0.0369</td>
<td>0.0313</td>
<td>0.0222</td>
<td>0.0222</td>
<td>0.0426</td>
</tr>
<tr>
<th>(0.6, 0.4)</th>
<td>0.0220</td>
<td>0.0379</td>
<td>0.0201</td>
<td>0.0190</td>
<td>0.0190</td>
<td>0.0314</td>
</tr>
<tr>
<th>(0.65, 0.35)</th>
<td>0.0218</td>
<td>0.0364</td>
<td>0.0104</td>
<td>0.0160</td>
<td>0.0160</td>
<td>0.0208</td>
</tr>
<tr>
<th>(0.7, 0.3)</th>
<td>0.0229</td>
<td>0.0371</td>
<td>0.0067</td>
<td>0.0119</td>
<td>0.0119</td>
<td>0.0096</td>
</tr>
<tr>
<th>(0.75, 0.25)</th>
<td>0.0250</td>
<td>0.0378</td>
<td>0.0161</td>
<td>0.0101</td>
<td>0.0101</td>
<td>0.0067</td>
</tr>
<tr>
<th>(0.8, 0.2)</th>
<td>0.0237</td>
<td>0.0333</td>
<td>0.0259</td>
<td>0.0082</td>
<td>0.0082</td>
<td>0.0143</td>
</tr>
<tr>
<th>(0.85, 0.15)</th>
<td>0.0227</td>
<td>0.0282</td>
<td>0.0381</td>
<td>0.0060</td>
<td>0.0060</td>
<td>0.0265</td>
</tr>
<tr>
<th>(0.9, 0.1)</th>
<td>0.0180</td>
<td>0.0202</td>
<td>0.0499</td>
<td>0.0049</td>
<td>0.0049</td>
<td>0.0382</td>
</tr>
<tr>
<th>(0.95, 0.05)</th>
<td>0.0097</td>
<td>0.0117</td>
<td>0.0607</td>
<td>0.0072</td>
<td>0.0072</td>
<td>0.0489</td>
</tr>
<tr>
<th>(1.0, 0.0)</th>
<td>0.0014</td>
<td>0.0024</td>
<td>0.0724</td>
<td>0.0103</td>
<td>0.0103</td>
<td>0.0606</td>
</tr>
</tbody>
</table>
> train: [0.79989987 0.20010013]
> validation: [0.80006676 0.19993324]
> evaluate_bin_sld: 197.725s
> evaluate_mul_sld: 53.526s
> kfcv: 40.971s
> atc_mc: 41.975s
> atc_ne: 41.358s
> doc_feat: 35.091s
> tot: 199.051s
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>bin</th>
<th>mul</th>
<th>kfcv</th>
<th>atc_mc</th>
<th>atc_ne</th>
<th>doc_feat</th>
</tr>
</thead>
<tbody>
<tr>
<th>(0.0, 1.0)</th>
<td>0.0009</td>
<td>0.0082</td>
<td>0.3148</td>
<td>0.0571</td>
<td>0.0571</td>
<td>0.3213</td>
</tr>
<tr>
<th>(0.05, 0.95)</th>
<td>0.0297</td>
<td>0.0223</td>
<td>0.2925</td>
<td>0.0492</td>
<td>0.0492</td>
<td>0.2991</td>
</tr>
<tr>
<th>(0.1, 0.9)</th>
<td>0.0283</td>
<td>0.0209</td>
<td>0.2733</td>
<td>0.0493</td>
<td>0.0493</td>
<td>0.2800</td>
</tr>
<tr>
<th>(0.15, 0.85)</th>
<td>0.0247</td>
<td>0.0182</td>
<td>0.2528</td>
<td>0.0447</td>
<td>0.0447</td>
<td>0.2596</td>
</tr>
<tr>
<th>(0.2, 0.8)</th>
<td>0.0216</td>
<td>0.0156</td>
<td>0.2328</td>
<td>0.0407</td>
<td>0.0407</td>
<td>0.2397</td>
</tr>
<tr>
<th>(0.25, 0.75)</th>
<td>0.0170</td>
<td>0.0136</td>
<td>0.2136</td>
<td>0.0425</td>
<td>0.0425</td>
<td>0.2205</td>
</tr>
<tr>
<th>(0.3, 0.7)</th>
<td>0.0146</td>
<td>0.0126</td>
<td>0.1941</td>
<td>0.0384</td>
<td>0.0384</td>
<td>0.2012</td>
</tr>
<tr>
<th>(0.35, 0.65)</th>
<td>0.0125</td>
<td>0.0113</td>
<td>0.1734</td>
<td>0.0331</td>
<td>0.0331</td>
<td>0.1806</td>
</tr>
<tr>
<th>(0.4, 0.6)</th>
<td>0.0113</td>
<td>0.0110</td>
<td>0.1510</td>
<td>0.0272</td>
<td>0.0272</td>
<td>0.1583</td>
</tr>
<tr>
<th>(0.45, 0.55)</th>
<td>0.0093</td>
<td>0.0135</td>
<td>0.1328</td>
<td>0.0247</td>
<td>0.0247</td>
<td>0.1402</td>
</tr>
<tr>
<th>(0.5, 0.5)</th>
<td>0.0088</td>
<td>0.0135</td>
<td>0.1131</td>
<td>0.0222</td>
<td>0.0222</td>
<td>0.1206</td>
</tr>
<tr>
<th>(0.55, 0.45)</th>
<td>0.0092</td>
<td>0.0155</td>
<td>0.0919</td>
<td>0.0207</td>
<td>0.0207</td>
<td>0.0995</td>
</tr>
<tr>
<th>(0.6, 0.4)</th>
<td>0.0092</td>
<td>0.0173</td>
<td>0.0742</td>
<td>0.0190</td>
<td>0.0190</td>
<td>0.0819</td>
</tr>
<tr>
<th>(0.65, 0.35)</th>
<td>0.0087</td>
<td>0.0178</td>
<td>0.0544</td>
<td>0.0161</td>
<td>0.0161</td>
<td>0.0621</td>
</tr>
<tr>
<th>(0.7, 0.3)</th>
<td>0.0093</td>
<td>0.0197</td>
<td>0.0323</td>
<td>0.0124</td>
<td>0.0124</td>
<td>0.0401</td>
</tr>
<tr>
<th>(0.75, 0.25)</th>
<td>0.0101</td>
<td>0.0218</td>
<td>0.0114</td>
<td>0.0093</td>
<td>0.0093</td>
<td>0.0187</td>
</tr>
<tr>
<th>(0.8, 0.2)</th>
<td>0.0117</td>
<td>0.0208</td>
<td>0.0098</td>
<td>0.0088</td>
<td>0.0088</td>
<td>0.0063</td>
</tr>
<tr>
<th>(0.85, 0.15)</th>
<td>0.0103</td>
<td>0.0178</td>
<td>0.0285</td>
<td>0.0064</td>
<td>0.0064</td>
<td>0.0204</td>
</tr>
<tr>
<th>(0.9, 0.1)</th>
<td>0.0103</td>
<td>0.0164</td>
<td>0.0480</td>
<td>0.0062</td>
<td>0.0062</td>
<td>0.0398</td>
</tr>
<tr>
<th>(0.95, 0.05)</th>
<td>0.0092</td>
<td>0.0117</td>
<td>0.0684</td>
<td>0.0071</td>
<td>0.0071</td>
<td>0.0601</td>
</tr>
<tr>
<th>(1.0, 0.0)</th>
<td>0.0011</td>
<td>0.0019</td>
<td>0.0887</td>
<td>0.0097</td>
<td>0.0097</td>
<td>0.0803</td>
</tr>
</tbody>
</table>
> train: [0.90003338 0.09996662]
> validation: [0.90003338 0.09996662]
> evaluate_bin_sld: 201.315s
> evaluate_mul_sld: 50.974s
> kfcv: 40.175s
> atc_mc: 41.663s
> atc_ne: 41.058s
> doc_feat: 35.055s
> tot: 202.573s
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>bin</th>
<th>mul</th>
<th>kfcv</th>
<th>atc_mc</th>
<th>atc_ne</th>
<th>doc_feat</th>
</tr>
</thead>
<tbody>
<tr>
<th>(0.0, 1.0)</th>
<td>0.0321</td>
<td>0.0184</td>
<td>0.6421</td>
<td>0.1336</td>
<td>0.1336</td>
<td>0.6454</td>
</tr>
<tr>
<th>(0.05, 0.95)</th>
<td>0.0835</td>
<td>0.0729</td>
<td>0.6056</td>
<td>0.1244</td>
<td>0.1244</td>
<td>0.6090</td>
</tr>
<tr>
<th>(0.1, 0.9)</th>
<td>0.1080</td>
<td>0.0976</td>
<td>0.5703</td>
<td>0.1204</td>
<td>0.1204</td>
<td>0.5739</td>
</tr>
<tr>
<th>(0.15, 0.85)</th>
<td>0.1154</td>
<td>0.0971</td>
<td>0.5354</td>
<td>0.1147</td>
<td>0.1147</td>
<td>0.5390</td>
</tr>
<tr>
<th>(0.2, 0.8)</th>
<td>0.1081</td>
<td>0.0916</td>
<td>0.5007</td>
<td>0.1064</td>
<td>0.1064</td>
<td>0.5045</td>
</tr>
<tr>
<th>(0.25, 0.75)</th>
<td>0.1032</td>
<td>0.0830</td>
<td>0.4632</td>
<td>0.1005</td>
<td>0.1005</td>
<td>0.4671</td>
</tr>
<tr>
<th>(0.3, 0.7)</th>
<td>0.0945</td>
<td>0.0775</td>
<td>0.4274</td>
<td>0.0916</td>
<td>0.0916</td>
<td>0.4313</td>
</tr>
<tr>
<th>(0.35, 0.65)</th>
<td>0.0966</td>
<td>0.0709</td>
<td>0.3914</td>
<td>0.0843</td>
<td>0.0843</td>
<td>0.3954</td>
</tr>
<tr>
<th>(0.4, 0.6)</th>
<td>0.0795</td>
<td>0.0639</td>
<td>0.3543</td>
<td>0.0748</td>
<td>0.0748</td>
<td>0.3584</td>
</tr>
<tr>
<th>(0.45, 0.55)</th>
<td>0.0735</td>
<td>0.0533</td>
<td>0.3210</td>
<td>0.0728</td>
<td>0.0728</td>
<td>0.3253</td>
</tr>
<tr>
<th>(0.5, 0.5)</th>
<td>0.0716</td>
<td>0.0473</td>
<td>0.2829</td>
<td>0.0633</td>
<td>0.0633</td>
<td>0.2873</td>
</tr>
<tr>
<th>(0.55, 0.45)</th>
<td>0.0550</td>
<td>0.0393</td>
<td>0.2465</td>
<td>0.0568</td>
<td>0.0568</td>
<td>0.2509</td>
</tr>
<tr>
<th>(0.6, 0.4)</th>
<td>0.0505</td>
<td>0.0317</td>
<td>0.2117</td>
<td>0.0509</td>
<td>0.0509</td>
<td>0.2162</td>
</tr>
<tr>
<th>(0.65, 0.35)</th>
<td>0.0403</td>
<td>0.0226</td>
<td>0.1741</td>
<td>0.0438</td>
<td>0.0438</td>
<td>0.1788</td>
</tr>
<tr>
<th>(0.7, 0.3)</th>
<td>0.0372</td>
<td>0.0178</td>
<td>0.1387</td>
<td>0.0348</td>
<td>0.0348</td>
<td>0.1434</td>
</tr>
<tr>
<th>(0.75, 0.25)</th>
<td>0.0262</td>
<td>0.0122</td>
<td>0.1009</td>
<td>0.0256</td>
<td>0.0256</td>
<td>0.1057</td>
</tr>
<tr>
<th>(0.8, 0.2)</th>
<td>0.0248</td>
<td>0.0110</td>
<td>0.0651</td>
<td>0.0194</td>
<td>0.0194</td>
<td>0.0701</td>
</tr>
<tr>
<th>(0.85, 0.15)</th>
<td>0.0181</td>
<td>0.0075</td>
<td>0.0298</td>
<td>0.0128</td>
<td>0.0128</td>
<td>0.0348</td>
</tr>
<tr>
<th>(0.9, 0.1)</th>
<td>0.0129</td>
<td>0.0093</td>
<td>0.0069</td>
<td>0.0080</td>
<td>0.0080</td>
<td>0.0037</td>
</tr>
<tr>
<th>(0.95, 0.05)</th>
<td>0.0077</td>
<td>0.0085</td>
<td>0.0426</td>
<td>0.0046</td>
<td>0.0046</td>
<td>0.0373</td>
</tr>
<tr>
<th>(1.0, 0.0)</th>
<td>0.0010</td>
<td>0.0010</td>
<td>0.0789</td>
<td>0.0088</td>
<td>0.0088</td>
<td>0.0735</td>
</tr>
</tbody>
</table>