Machine Learning, Support Vector Machine, Support Vector Classifier, Data Scie, Svm.
np.random.normal(0, 0.1, len(y)) noise_2 = np.random.normal(0, 0.1, len(y)) X[:, 1] = X[:, 1] + noise_1 X[:, 0] = X[:, 0] + noise_2 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20) Now very important point. We always have to apply feature scaling for SVM, otherwise the