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- import numpy as np
- import matplotlib.pyplot as plt
- from sklearn.model_selection import learning_curve
- def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
- n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):
- plt.figure()
- plt.title(title)
- if ylim is not None:
- plt.ylim(*ylim)
- plt.xlabel("Training examples")
- plt.ylabel("Score")
- train_sizes, train_scores, test_scores = learning_curve(
- estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
- train_scores_mean = np.mean(train_scores, axis=1)
- train_scores_std = np.std(train_scores, axis=1)
- test_scores_mean = np.mean(test_scores, axis=1)
- test_scores_std = np.std(test_scores, axis=1)
- plt.grid()
- plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
- train_scores_mean + train_scores_std, alpha=0.1,
- color="r")
- plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
- test_scores_mean + test_scores_std, alpha=0.1, color="g")
- plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
- label="Training score")
- plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
- label="Cross-validation score")
- plt.legend(loc="best")
- return plt
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