adaboost交叉验证求最优迭代次数Python代码
时间: 2023-06-13 14:06:01 浏览: 53
以下是基于Scikit-learn库的Adaboost交叉验证求最优迭代次数的Python代码:
```python
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV, cross_val_score
# 创建决策树分类器
dt = DecisionTreeClassifier(max_depth=1)
# 创建AdaBoost分类器
clf = AdaBoostClassifier(base_estimator=dt)
# 设置要调整的参数选项
param_grid = {'n_estimators': [50, 100, 150, 200]}
# 使用GridSearchCV进行交叉验证
grid_search = GridSearchCV(clf, param_grid=param_grid, cv=5)
grid_search.fit(X, y)
# 打印最优迭代次数和对应的交叉验证得分
print("最优迭代次数:", grid_search.best_params_['n_estimators'])
print("交叉验证得分:", grid_search.best_score_)
```
其中,`X`和`y`分别为特征矩阵和标签向量。`GridSearchCV`会自动进行交叉验证,并返回最优参数选项和对应的交叉验证得分。