sklearn.model_selection中learning_curve编程示例
时间: 2023-05-13 17:04:27 浏览: 79
可以参考以下代码:
from sklearn.model_selection import learning_curve
import numpy as np
import matplotlib.pyplot as plt
# 创建一个简单的分类器
from sklearn.naive_bayes import GaussianNB
clf = GaussianNB()
# 创建一个数据集
X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
y = np.array([0, 1, 0, 1])
# 定义训练集大小的范围
train_sizes = np.linspace(0.1, 1.0, 5)
# 计算学习曲线
train_sizes, train_scores, test_scores = learning_curve(clf, X, y, train_sizes=train_sizes)
# 计算训练集和测试集的平均得分
train_scores_mean = np.mean(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
# 绘制学习曲线
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.xlabel("Training examples")
plt.ylabel("Score")
plt.legend(loc="best")
plt.show()