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decision_function

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

Get predicted confidence scores 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 decision_function 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
Predicted confidence scores 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("kSVM", metric="accuracy")

# Predict confidence scores on new data
predictions = atom.ksvm.decision_function(X_new)
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