def predict(self, X):
        """
        Predict the less costly class for a given observation
        
        Parameters
        ----------
        X : array (n_samples, n_features)
            Data for which to predict minimum cost label.
        
        Returns
        -------
        y_hat : array (n_samples,)
            Label with expected minimum cost for each observation.
        """
        X = _check_2d_inp(X)
        return np.argmax(self.decision_function(X), axis=1)
def predict ( self , X ) : X = _check_2d_inp ( X ) return np . argmax ( self . decision_function ( X ) , axis = 1 )
Predict the less costly class for a given observation
        
        Parameters
        ----------
        X : array (n_samples, n_features)
            Data for which to predict minimum cost label.
        
        Returns
        -------
        y_hat : array (n_samples,)
            Label with expected minimum cost for each observation.