def predict(self, X):
    """Predict class labels for samples in X.
    Parameters
    ----------
    X : {array-like, sparse matrix}, shape = [n_samples, n_features]
        Samples.
    Returns
    -------
    C : array, shape = [n_samples]
        Predicted class label per sample.
    """
scores = self.decision_function(X)
if len(scores.shape) == 1:
    indices = (scores > 0).astype(np.int)
else:
    indices = scores.argmax(axis=1)
return self.classes_[indices]
How to fix the false positives rate of a linear SVM?