1. 过拟合问题可以通过调整机器学习的参数来完成，比如sklearn中通过调节gamma参数，将训练损失和测试损失降到最低
2. 代码实现（显示gamma参数对训练损失和测试损失的影响）
from __future__ import print_function 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()
原文：https://www.cnblogs.com/liuzhiqaingxyz/p/9615540.html