from sklearn.learning_curve import validation_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
param_range = np.logspace(-6, -2.3, 5)
train_loss, test_loss = validation_curve(SVC(), X,y, param_name='gamma', param_range=param_range,cv=10, scoring='mean_squared_error')
train_loss_mean = -np.mean(train_loss, axis=1)
test_loss_mean = -np.mean(test_loss, axis=1)
plt.plot(param_range, train_loss_mean, 'o-', color="r",
label="Training")
plt.plot(param_range, test_loss_mean, 'o-', color="g",
label="Cross-validation")
plt.xlabel("gamma")
plt.ylabel("Loss")
plt.legend(loc="best")
plt.show()
https://morvanzhou.github.io/tutorials/machine-learning/sklearn/3-4-cross-validation3/
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