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Updated UCI binary notes

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Lorenzo Volpi 2024-07-02 16:33:27 +02:00
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docs/source/wiki_editable/Datasets.md
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@ -243,24 +243,15 @@ are summarized below.
| wine-q-white | 2 | 4898 | 11 | [0.335, 0.665] | dense |
| yeast | 2 | 1484 | 8 | [0.711, 0.289] | dense |
### Issues:
#### Notes:
All datasets will be downloaded automatically the first time they are requested, and
stored in the _quapy_data_ folder for faster further reuse.
However, some datasets require special actions that at the moment are not fully
automated.
* Datasets with ids "ctg.1", "ctg.2", and "ctg.3" (_Cardiotocography Data Set_) load
an Excel file, which requires the user to install the _xlrd_ Python module in order
to open it.
* The dataset with id "pageblocks.5" (_Page Blocks Classification (5)_) needs to
open a "unix compressed file" (extension .Z), which is not directly doable with
standard Pythons packages like gzip or zip. This file would need to be uncompressed using
OS-dependent software manually. Information on how to do it will be printed the first
time the dataset is invoked.
* It is a good idea to ignore datasets _acute.a_, _acute.b_ and _balance.2_, since the former two
are very easy (many classifiers would score 100% accuracy) while the latter is extremely difficult
(probably there is some problem with this dataset, the errors it tends to produce are orders of magnitude
greater than for other datasets, and this has a disproportionate impact in the average performance).
However, notice that it is a good idea to ignore datasets:
* _acute.a_ and _acute.b_: these are very easy and many classifiers would score 100% accuracy
* _balance.2_: this is extremely difficult; probably there is some problem with this dataset,
the errors it tends to produce are orders of magnitude greater than for other datasets,
and this has a disproportionate impact in the average performance.
### Multiclass datasets