olpy/classifiers/__base.py
def score(self, X, y):
        """Compute the score performed on the dataset.

        Given a labelled data, this method evaluate the performance of
        the models by predicting and computing the accuracy score.
        
        Note:
            Different models can override the function to return a 
            different metric.

        Args:
            X (:obj:`numpy.ndarray`): Input data with n rows and
                m columns
            Y (:obj:`, `numpy.ndarray`): Output variable with binary
                labels.

        Returns:
            float: The accuracy score of the model given the labelled
            data.
        """
        return np.count_nonzero(self.predict(X) == y) / X.shape[0]
Compute the score performed on the dataset.

        Given a labelled data, this method evaluate the performance of
        the models by predicting and computing the accuracy score.
        
        Note:
            Different models can override the function to return a 
            different metric.

        Args:
            X (:obj:`numpy.ndarray`): Input data with n rows and
                m columns
            Y (:obj:`, `numpy.ndarray`): Output variable with binary
                labels.

        Returns:
            float: The accuracy score of the model given the labelled
            data.
        
Compute the score performed on the dataset.
Given a labelled data, this method evaluate the performance of
the models by predicting and computing the accuracy score.

Different models can override the function to return a
different metric.
Compute the score performed on the dataset . Given a labelled data this method evaluate the performance of the models by predicting and computing the accuracy score . Different models can override the function to return a different metric .
def score(self, X, y):
        return np.count_nonzero(self.predict(X) == y) / X.shape[0]
def score ( self , X , y ) : return np . count_nonzero ( self . predict ( X ) == y ) / X . shape [ 0 ]
Compute the score performed on the dataset.
Compute the score performed on the dataset .
"""Compute the score performed on the dataset.

        Given a labelled data, this method evaluate the performance of
        the models by predicting and computing the accuracy score.
        
        Note:
            Different models can override the function to return a 
            different metric.

        Args:
            X (:obj:`numpy.ndarray`): Input data with n rows and
                m columns
            Y (:obj:`, `numpy.ndarray`): Output variable with binary
                labels.

        Returns:
            float: The accuracy score of the model given the labelled
            data.
        """
The accuracy score of the model given the labelled
data.
The accuracy score of the model given the labelled data .
float
self
X
y
X (
`numpy.ndarray`): Input data with n rows and
m columns
` numpy . ndarray ` ) : Input data with n rows and m columns
Y (
`, `numpy.ndarray`): Output variable with binary
labels.
` ` numpy . ndarray ` ) : Output variable with binary labels .