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predict_proba

method predict_proba(X, verbose=None) [source]

Get class probabilities on unseen data or rows in the dataset. New data is first transformed through the model's pipeline. Transformers that are only applied on the training set are skipped. If called from a trainer, the best model in the pipeline (under the winner attribute) is used. If called from a model, that model is used. The estimator must have a predict_proba method.

Parameters:

X: int, str, slice, sequence or dataframe-like
Index names or positions of rows in the dataset, or unseen feature set with shape=(n_samples, n_features).

verbose: int or None, optional (default=None)
Verbosity level of the output. If None, it uses the transformer's own verbosity.

Returns: np.array
The class probabilities of the input samples, with shape=(n_samples,) for binary classification tasks and (n_samples, n_classes) for multiclass classification tasks.


Example

from atom import ATOMClassifier

atom = ATOMClassifier(X, y)
atom.run(["Tree", "AdaB"], metric="AP", n_calls=10)

# Make predictions on new data
predictions = atom.adab.predict_proba(X_new)
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