olpy/classifiers/__base.py
def decision_function(self, X):
        """Compute the values of the decision boundaries.

        Given an unlabelled data, this method computes the probabilities
        of being assigned the positive label.

        Args:
            X (:obj:`numpy.ndarray`): Input data with n rows and
                    m columns

        Returns:
            list[float]: The probabilities for each data point to be
            of the positive class label.
        """
        return X @ self.weights
Compute the values of the decision boundaries.

        Given an unlabelled data, this method computes the probabilities
        of being assigned the positive label.

        Args:
            X (:obj:`numpy.ndarray`): Input data with n rows and
                    m columns

        Returns:
            list[float]: The probabilities for each data point to be
            of the positive class label.
        
Compute the values of the decision boundaries.
Given an unlabelled data, this method computes the probabilities
of being assigned the positive label.
Compute the values of the decision boundaries . Given an unlabelled data this method computes the probabilities of being assigned the positive label .
def decision_function(self, X):
        return X @ self.weights
def decision_function ( self , X ) : return X @ self . weights
Compute the values of the decision boundaries.
Compute the values of the decision boundaries .
"""Compute the values of the decision boundaries.

        Given an unlabelled data, this method computes the probabilities
        of being assigned the positive label.

        Args:
            X (:obj:`numpy.ndarray`): Input data with n rows and
                    m columns

        Returns:
            list[float]: The probabilities for each data point to be
            of the positive class label.
        """
The probabilities for each data point to be
of the positive class label.
The probabilities for each data point to be of the positive class label .
list[float]
self
X
X (
`numpy.ndarray`): Input data with n rows and
m columns
` numpy . ndarray ` ) : Input data with n rows and m columns