训练集和测试集学习曲线代码
时间: 2024-04-14 11:21:18 浏览: 23
要绘制训练集和测试集的学习曲线,你可以使用不同的模型性能指标(如准确率、损失函数等)随着训练集大小的增加而变化的曲线来表示。
下面是一个示例代码,用于使用scikit-learn库绘制训练集和测试集的学习曲线:
```python
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import learning_curve
def plot_learning_curve(estimator, X, y):
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, train_sizes=np.linspace(0.1, 1.0, 10), cv=5)
train_mean = np.mean(train_scores, axis=1)
train_std = np.std(train_scores, axis=1)
test_mean = np.mean(test_scores, axis=1)
test_std = np.std(test_scores, axis=1)
plt.figure()
plt.plot(train_sizes, train_mean, 'o-', color='r', label='Training score')
plt.plot(train_sizes, test_mean, 'o-', color='g', label='Cross-validation score')
plt.fill_between(train_sizes, train_mean - train_std,
train_mean + train_std, alpha=0.1, color='r')
plt.fill_between(train_sizes, test_mean - test_std,
test_mean + test_std, alpha=0.1, color='g')
plt.xlabel('Training Set Size')
plt.ylabel('Score')
plt.legend(loc='best')
plt.grid()
plt.show()
# 使用示例
from sklearn.datasets import load_digits
from sklearn.svm import SVC
digits = load_digits()
X, y = digits.data, digits.target
svm = SVC(gamma='scale')
plot_learning_curve(svm, X, y)
```
这段代码将绘制出训练集和交叉验证集的学习曲线,并显示训练集大小和性能指标之间的关系。
相关推荐
![rar](https://img-home.csdnimg.cn/images/20210720083606.png)
![py](https://img-home.csdnimg.cn/images/20210720083646.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)