learning_curve:

作用：模型精度和不同大小資料集之間的關係

from sklearn.model_selection import learning_curve
train_sizes, train_scores, test_scores = learning_curve(estimator=pipe_clf,
             X=X_train, y=y_train, train_sizes=np.linspace(0.1, 1.0, 10), cv=10, n_jobs=1)
train_mean = np.mean(train_scores, axis=1)  #cv=10,，每行有10個score
train_std = np.std(train_scores, axis=1)
test_mean = np.mean(test_scores, axis=1)
test_std = np.std(test_scores, axis=1)
plt.plot(train_sizes, train_mean, color='blue', marker='o', markersize=5, label='training accuracy')
plt.fill_between(train_sizes, train_mean + train_std, train_mean - train_std, alpha=0.15, color='blue') #alpha是透明度
plt.plot(train_sizes, test_mean, color='green', linestyle='--', marker='s', markersize=5, label='validation accuracy')
plt.fill_between(train_sizes, test_mean + test_std, test_mean - test_std, alpha=0.15, color='green')
plt.grid()
plt.xlabel('Number of training samples')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')
plt.ylim([0.8, 1.0])
plt.title('learning curve')
 

validation_curve

作用：模型精度和不同引數之間關係

from sklearn.model_selection import validation_curve
param_range = [0.001, 0.01, 0.1, 1.0, 10.0, 100.0]
train_scores, test_scores = validation_curve(estimator=SVC, X=X_train, y=y_train, 
	                        param_name='clf__C', param_range=param_range, cv=10)  #C是SVC的懲罰引數
train_mean = np.mean(train_scores, axis=1)
train_std = np.std(train_scores, axis=1)
test_mean = np.mean(test_scores, axis=1)
test_std = np.std(test_scores, axis=1)
plt.figure(2)
plt.plot(param_range, train_mean, color='blue', marker='o', markersize=5, label='training accuracy')
plt.fill_between(param_range, train_mean + train_std, train_mean - train_std, alpha=0.15, color='blue')
plt.plot(param_range, test_mean, color='green', linestyle='--', marker='s', markersize=5, label='validation accuracy')
plt.fill_between(param_range, test_mean + test_std, test_mean - test_std, alpha=0.15, color='green')
plt.grid()
plt.xscale('log')   #x軸是對數軸
plt.legend(loc='lower right')
plt.xlabel('Parameter C')
plt.ylabel('Accuracy')
plt.ylim([0, 1.0])
plt.title('validation curve')
plt.show()
 

參考資料

機器學習系統模型調優實戰--所有調優技術都附相應的scikit-learn實現:https://blog.csdn.net/xlinsist/article/details/51344449