Method predict_proba

mne/decoding/search_light.py:244–264

Predict each data slice with a series of independent estimators. The number of tasks in X should match the number of tasks/estimators given at fit time. Parameters ---------- X : array, shape (n_samples, nd_features, n_tasks) The input samples. F

(self, X)

        return self._transform(X, "predict")

    def predict_proba(self, X):
        """Predict each data slice with a series of independent estimators.

        The number of tasks in X should match the number of tasks/estimators
        given at fit time.

        Parameters
        ----------
        X : array, shape (n_samples, nd_features, n_tasks)
            The input samples. For each data slice, the corresponding estimator
            makes the sample probabilistic predictions, e.g.:
            ``[estimators[ii].predict_proba(X[..., ii]) for ii in range(n_estimators)]``.
            The feature dimension can be multidimensional e.g.
            X.shape = (n_samples, n_features_1, n_features_2, n_tasks).

        Returns
        -------
        y_pred : array, shape (n_samples, n_tasks, n_classes)
            Predicted probabilities for each estimator/data slice/task.
        """  # noqa: E501
        return self._transform(X, "predict_proba")

    def decision_function(self, X):
        """Estimate distances of each data slice to the hyperplanes.