def fit_predict(self, features, target, sample_weight=None, groups=None):
        """Call fit and predict in sequence.

        Parameters
        ----------
        features: array-like {n_samples, n_features}
            Feature matrix
        target: array-like {n_samples}
            List of class labels for prediction
        sample_weight: array-like {n_samples}, optional
            Per-sample weights. Higher weights force TPOT to put more emphasis on those points
        groups: array-like, with shape {n_samples, }, optional
            Group labels for the samples used when performing cross-validation.
            This parameter should only be used in conjunction with sklearn's Group cross-validation
            functions, such as sklearn.model_selection.GroupKFold

        Returns
        ----------
        array-like: {n_samples}
            Predicted target for the provided features

        """
        self.fit(features, target, sample_weight=sample_weight, groups=groups)

        return self.predict(features)
Call fit and predict in sequence.

        Parameters
        ----------
        features: array-like {n_samples, n_features}
            Feature matrix
        target: array-like {n_samples}
            List of class labels for prediction
        sample_weight: array-like {n_samples}, optional
            Per-sample weights. Higher weights force TPOT to put more emphasis on those points
        groups: array-like, with shape {n_samples, }, optional
            Group labels for the samples used when performing cross-validation.
            This parameter should only be used in conjunction with sklearn's Group cross-validation
            functions, such as sklearn.model_selection.GroupKFold

        Returns
        ----------
        array-like: {n_samples}
            Predicted target for the provided features