生成DecisionTreeClassifier参数调优python代码

很高兴为您解答。以下是生成DecisionTreeClassifier参数调优Python代码的示例：

from sklearn.tree import DecisionTreeClassifier
from sklearn import datasets
from sklearn.model_selection import GridSearchCV

# 加载数据集
iris = datasets.load_iris()

# 分割训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, 
                                                    test_size=0.3, random_state=42)

# 定义参数范围
param_grid = {'max_depth': range(1, 10),
              'min_samples_split': range(2, 10),
              'min_samples_leaf': range(1, 5)}

# 定义分类器
dtc = DecisionTreeClassifier()

# 定义GridSearchCV
grid_search = GridSearchCV(dtc, param_grid=param_grid, cv=5)

# 拟合参数
grid_search.fit(X_train, y_train)

# 打印最佳参数
print(grid_search.best_params_)

# 预测并打印准确率
print(grid_search.score(X_test, y_test))

希望以上示例代码能帮到您，如有需要请随时提出。