Skip to content

predict_proba

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

Transform new data through the current branch and return class probabilities. 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: dict, list, tuple, np.array, sps.matrix or pd.DataFrame
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: p: np.ndarray
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)
Back to top