from sklearn.learning_curve import learning_curve
from sklearn.datasets import load_digits
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import numpy as np
digits = load_digits()
X = digits.data
y = digits.target
train_sizes, train_loss, test_loss = learning_curve(SVC(gamma=0.01), X,y,cv=10, scoring='mean_squared_error', train_sizes=[0.1, 0.25, 0.5, 0.75, 1])
train_loss_mean = -np.mean(train_loss, axis=1)
test_loss_mean = -np.mean(test_loss, axis=1)
plt.plot(train_sizes, train_loss_mean, 'o-', color="r",
label="Training")
plt.plot(train_sizes, test_loss_mean, 'o-', color="g",
label="Cross-validation")
plt.xlabel("Training examples")
plt.ylabel("Loss")
plt.legend(loc="best")
plt.show()
过拟合可以通过交叉验证来确认
https://morvanzhou.github.io/tutorials/machine-learning/sklearn/3-3-cross-validation2/
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