aefd8bcf146d6de8f19fc0c3c5873880bc82886f,mlxtend/sklearn/ensemble.py,EnsembleClassifier,fit,#EnsembleClassifier#Any#Any#,86

Before Change

            clf.fit(X, y)
        
        self.le.fit(y)
        self.classes_ = np.unique(y)
                
        return self
            
    def predict(self, X):

After Change

        self : object
        

        if isinstance(y, np.ndarray) and len(y.shape) > 1 and y.shape[1] > 1:
            raise NotImplementedError("Multilabel classification"
                                      " not implemented, yet.")

        if self.voting not in ("soft", "hard"):
            raise ValueError("Voting must be "soft" or "hard" got (voting=%r)"
                             % voting)

        if self.weights and len(self.weights) != len(self.clfs):
            raise ValueError("Number of classifiers and weights must be equal"
                             " got %d weights, %d clfs"
                             % (len(self.weights), len(self.clfs)))

        self.le_ = LabelEncoder()
        self.le_.fit(y)
        self.classes_ = self.le_.classes_

        for clf in self.clfs:

Instances