使用MLFlow执行GridSearchCV

import mlflow
from sklearn.model_selection import GridSearchCV

model = YourModel()  # 替换为你要使用的模型
param_grid = {
    'param1': [value1, value2, ...],  # 替换为你要调优的参数及其取值范围
    'param2': [value1, value2, ...],
    ...
}

mlflow.set_experiment('Your Experiment Name')  # 替换为你的实验名称

grid_search = GridSearchCV(model, param_grid, cv=5)  # 替换为你的模型和参数网格
with mlflow.start_run():
    grid_search.fit(X_train, y_train)  # 替换为你的训练数据

mlflow.log_params(grid_search.best_params_)  # 记录最佳参数
mlflow.log_metric('mean_test_score', grid_search.best_score_)  # 记录最佳得分