文章作者：Tyan 博客：noahsnail.com | | 简书
本文主要介绍scikit-learn中的交叉验证。
Demo import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_digits from sklearn.cross_validation import train_test_split from sklearn.svm import SVC from sklearn.learning_curve import validation_curve from sklearn.model_selection import cross_val_score # 选取合适的参数gamma # 加载数据集 digits = load_digits() X = digits.data y = digits.target # 定义gamma参数 param_range = np.logspace(-6, -2.3, 5) # 用SVM进行学习并记录loss train_loss, test_loss = validation_curve(SVC(), X, y, param_name = 'gamma', param_range = param_range, cv = 10, scoring = 'mean_squared_error') # 训练误差均值 train_loss_mean = -np.mean(train_loss, axis = 1) # 测试误差均值 test_loss_mean = -np.mean(test_loss, axis = 1) # 绘制误差曲线 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() 结果