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decision_function

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

Transform new data through all transformers in the current branch and return predicted confidence scores. 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: dict, list, tuple, np.ndarray or pd.DataFrame
Feature set with shape=(n_samples, n_features).

pipeline: bool, sequence or None, optional (default=None)
Transformers to use on the data before predicting.
  • If None: Only transformers that are applied on the whole dataset are used.
  • If False: Don't use any transformers.
  • If True: Use all transformers in the pipeline.
  • If sequence: Transformers to use, selected by their index in the pipeline.

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
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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