现有划分好的训练集X_train,y_train和测试集X_test,y_test,编写代码:构建回归树模型,逐步对max_depth和min_samples_leaf调参,绘制学习曲线,输出最佳参数
时间: 2023-07-20 20:35:15 浏览: 54
好的,我会根据您提供的要求编写代码。以下是Python代码:
```python
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import learning_curve, GridSearchCV
import matplotlib.pyplot as plt
# 定义回归树模型
model = DecisionTreeRegressor(random_state=42)
# 定义超参数范围
param_grid = {'max_depth': range(1, 11),
'min_samples_leaf': range(1, 11)}
# 使用GridSearchCV进行网格搜索
grid_search = GridSearchCV(model, param_grid, cv=5, scoring='neg_mean_squared_error')
grid_search.fit(X_train, y_train)
# 输出最佳参数
print("Best parameters: ", grid_search.best_params_)
# 绘制学习曲线
train_sizes, train_scores, test_scores = learning_curve(grid_search.best_estimator_, X_train, y_train, cv=5)
train_scores_mean = -train_scores.mean(axis=1)
test_scores_mean = -test_scores.mean(axis=1)
plt.plot(train_sizes, train_scores_mean, 'o-', color="r", label="Training")
plt.plot(train_sizes, test_scores_mean, 'o-', color="g", label="Cross-validation")
plt.legend(loc="best")
plt.show()
```
这段代码使用了sklearn中的DecisionTreeRegressor作为回归树模型,使用GridSearchCV进行超参数调优,并绘制了学习曲线。您只需要将X_train, y_train和X_test, y_test替换为您的数据即可。
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