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

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

        Returns
        -------
        prob_preds : array_like
            The predicted logistic regression scores for each sample
            in the features array
        """

        # Reshape the features to concatenate unity feature vector
        features = self._reshape_features(features)

        # Use the sigmoid function
        prob_preds = 1 / (1 + np.exp(-features @ self.params))

        return prob_preds
Predict the scores for each sample in the features arr

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

        Returns
        -------
        prob_preds : array_like
            The predicted logistic regression scores for each sample
            in the features array
        
Predict the scores for each sample in the features arr
Predict the scores for each sample in the features arr
def predict_proba(self, features):
        features = self._reshape_features(features)
        prob_preds = 1 / (1 + np.exp(-features @ self.params))
        return prob_preds
def predict_proba ( self , features ) : features = self . _reshape_features ( features ) prob_preds = 1 / ( 1 + np . exp ( - features @ self . params ) ) return prob_preds
Predict the scores for each sample in the features arr
Predict the scores for each sample in the features arr
"""Predict the scores for each sample in the features arr

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

        Returns
        -------
        prob_preds : array_like
            The predicted logistic regression scores for each sample
            in the features array
        """ # Reshape the features to concatenate unity feature vector
 # Use the sigmoid function

The predicted logistic regression scores for each sample
in the features array
The predicted logistic regression scores for each sample in the features array
array_like

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