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maomlab
/

AttentiveSkin

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Tabular Classification

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Dataset card Data Studio Files Files and versions

haneulpark commited on Jun 3, 2024

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7ed241f

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@@ -198,8 +198,12 @@ then load, featurize, split, fit, and evaluate the catboost model
     scores = classification_suite.compute(
         references=split_featurised_dataset["test"]['GHS'],
         predictions=preds["cat_boost_classifier::GHS"])
 ## AttentiveSkin
 To Predict Skin Corrosion/Irritation Potentials of Chemicals via Explainable Machine Learning Methods
 Download: https://github.com/BeeBeeWong/AttentiveSkin/releases/tag/v1.0

     scores = classification_suite.compute(
         references=split_featurised_dataset["test"]['GHS'],
         predictions=preds["cat_boost_classifier::GHS"])
+### Data splits
+Here we have used the Realistic Split method described in (Martin et al., 2018).
 ## AttentiveSkin
 To Predict Skin Corrosion/Irritation Potentials of Chemicals via Explainable Machine Learning Methods
 Download: https://github.com/BeeBeeWong/AttentiveSkin/releases/tag/v1.0