umbmid/ai/logreg.py
def predict_labels(self, features):
        """Predict the class labels for each sample in the features arr

        Parameters
        ----------
        features : array_like
            The features for each sample

        Returns
        -------
        label-preds : array_like
            The predicted class labels for each sample in the features
            array
        """

        # Round the predicted probabilities to obtain the predicted
        # classes
        label_preds = np.round(self.predict_proba(features)).astype(int)

        return label_preds
Predict the class labels for each sample in the features arr

        Parameters
        ----------
        features : array_like
            The features for each sample

        Returns
        -------
        label-preds : array_like
            The predicted class labels for each sample in the features
            array
        
Predict the class labels for each sample in the features arr
Predict the class labels for each sample in the features arr
def predict_labels(self, features):
        label_preds = np.round(self.predict_proba(features)).astype(int)
        return label_preds
def predict_labels ( self , features ) : label_preds = np . round ( self . predict_proba ( features ) ) . astype ( int ) return label_preds
Predict the class labels for each sample in the features arr
Predict the class labels for each sample in the features arr
"""Predict the class labels for each sample in the features arr

        Parameters
        ----------
        features : array_like
            The features for each sample

        Returns
        -------
        label-preds : array_like
            The predicted class labels for each sample in the features
            array
        """ # Round the predicted probabilities to obtain the predicted
 # classes

The predicted class labels for each sample in the features
array
The predicted class labels for each sample in the features array
array_like

self
features
The features for each sample
The features for each sample