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feat: Add a predict_proba method on SKLearnClassifier #21556

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feat: Add a predict_proba method on SKLearnClassifier #21556

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8 changes: 8 additions & 0 deletions keras/src/wrappers/sklearn_wrapper.py
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Expand Up @@ -278,6 +278,14 @@ def dynamic_model(X, y, loss, layers=[10]):
```
"""

def predict_proba(self, X):
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The problem here is that since the model is configurable, we have no way to know whether the model outputs probabilities or not. This method serves no additional purpose over just predict().

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@divakaivan divakaivan Aug 7, 2025 •
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@fchollet Could you elaborate, please? I'm not sure I understand your comment. In the case when the user expects probas they will get probas. The only difference between this predict_proba and predict is that the target is not transformed back.

If the user expects probabilities, then they will get them. Although predict_proba might not always return proper probabilities, its inclusion allows users to interoperate with sklearn workflows that expect it. Some examples are in the original issue request.

"""Predict class probabilities of the input samples X."""
from sklearn.utils.validation import check_is_fitted

check_is_fitted(self)
X = _validate_data(self, X, reset=False)
return self.model_.predict(X)

def _process_target(self, y, reset=False):
"""Classifiers do OHE."""
target_type = type_of_target(y, raise_unknown=True)
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