color='r', marker='s', label='-1') plt.ylim(-3.0) plt.legend() plt.show() X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) sc = StandardScaler() sc.fit(X_train) X_train_std = sc.transform(X_train) X_test_std = sc.transform(X_test) ml = SVC(kernel='rbf', C=10.0, gamma=1.0, random_state=0) ml.fit(X_train_std, y_train) y_pred = ml.predict(X_test_std) print('총 테스트 개수: %d, 오류개수:%d' % (len(y_test), (y_test != y_pred).sum())) print('정확도: %.2f' % accuracy_score(y_test, y_pred)) X_combined_std = np.vstack((X_train_std, X_test_std)) y_combined = np.hstack((y_train, y_test)) plot_decision_region(X=X_combined_std, y=y_combined, classifier=ml, test_idx=range(105, 150), title='scikit-learn SVM kernel=rbf')
random_state=0) sc = StandardScaler() sc.fit(X_train) X_train_std = sc.transform(X_train) X_test_std = sc.transform(X_test) # ml = Perceptron(eta0=0.01, max_iter=40, tol=0, random_state=0) # ml = LogisticRegression(C=1000.0, random_state=0) # ml = SVC(kernel='linear', C=1.0, random_state=0) # ml = SGDClassifier(loss = 'perceptron') # ml = SGDClassifier(loss='log') # ml = SGDClassifier(loss='hinge') # ml = DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0) # ml = RandomForestClassifier(criterion='entropy', n_estimators=10, n_jobs=2, random_state=1) ml = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski') ml.fit(X_train_std, y_train) y_pred = ml.predict(X_test_std) print('총 테스트 개수: %d, 오류개수:%d' % (len(y_test), (y_test != y_pred).sum())) print('정확도: %.2f' % accuracy_score(y_test, y_pred)) X_combined_std = np.vstack((X_train_std, X_test_std)) y_combined = np.hstack((y_train, y_test)) plot_decision_region(X=X_combined_std, y=y_combined, classifier=ml, test_idx=range(105, 150), title='Random Forest Classifier')
if __name__ == '__main__': iris = datasets.load_iris() X = iris.data[:, [2, 3]] y = iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) sc = StandardScaler() sc.fit(X_train) X_train_std = sc.transform(X_train) X_test_std = sc.transform(X_test) ml = LogisticRegression(C=1000.0, random_state=0) ml.fit(X_train_std, y_train) y_pred = ml.predict(X_test_std) print('총 테스트 개수: %d, 오류개수:%d' % (len(y_test), (y_test != y_pred).sum())) print('정확도: %2f' % accuracy_score(y_test, y_pred)) X_combined_std = np.vstack((X_train_std, X_test_std)) y_combined_std = np.hstack((y_train, y_test)) plot_decision_region(X=X_combined_std, y=y_combined_std, classifier=ml, test_idx=range(105, 150), title='scikit-learn Logistic Regression')
import numpy as np from mylib.plotdregion import plot_decision_region if __name__ == '__main__': iris = datasets.load_iris() X = iris.data[:, [2, 3]] y = iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) sc = StandardScaler() sc.fit(X_train) X_train_std = sc.transform(X_train) X_test_std = sc.transform(X_test) ml = Perceptron(eta0=0.01, max_iter=40, tol=0, random_state=0) ml.fit(X_train_std, y_train) y_pred = ml.predict(X_test_std) print('총 테스트 개수: %d, 오류개수:%d' % (len(y_test), (y_test != y_pred).sum())) print('정확도: %.2f' % accuracy_score(y_test, y_pred)) X_combined_std = np.vstack((X_train_std, X_test_std)) y_combined = np.hstack((y_train, y_test)) plot_decision_region(X=X_combined_std, y=y_combined, classifier=ml, test_idx=range(105, 150), title='scikit-learn Perceptron')