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| 描述 |
Python脚本 |
|
| 导入python包 |
进口xgboost xgb |
| 从xgboost进口XGBRegressor |
| 从sklearn。model_selection进口train_test_split cross_val_score RepeatedKFold |
| 从sklearn。指标导入mean_squared_error MSE |
| 解释框架的特性 |
框架= XGBRegressor(客观=“reg: squarederror”) |
| #的解释评价机制 |
| 简历= RepeatedKFold (n_splits = 10, n_repeats = 3, random_state = 1) |
| #框架评估 |
| n_scores = cross_val_score(框架、x_train UCS_train) |
| 打印(“美:%。8 f (% .8f) %(意思是(n_scores)、性病(n_scores)) |
| #适合训练数据集上的框架 |
| 框架= XGBRegressor(客观=“reg: squarederror”) |
| 框架。fit (x, UCS) |
| 创建网格的参数 |
gbm_param_grid = { |
| “埃塔”:[0.2,0.25,0.3,0.35,0.4,0.5,0.65,0.7,0.8,0.9), |
| “max_depth”: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10), |
| “colsample_bytree”: [0.5, 0.55, 0.6, 0.62, 0.65, 0.7, 0.8, 0.9, 0.95, 1], |
| “子”:[0.5,0.55,0.6,0.62,0.65,0.7,0.8,0.9,0.95,1], |
| } |
| #实例化回归量:“绿带运动” |
| “绿带运动”= xgb.XGBRegressor(客观=“reg: squarederror”) |
| #实现网格搜索:grid_mse |
| randomized_mse = RandomizedSearchCV ( |
| 估计量=“绿带运动”, |
| param_distributions = gbm_param_grid, |
| 得分= " neg_mean_squared_error ", |
| 简历= 3, |
| verbose = 1, |
| ) |
| #适合grid_mse UCS训练数据集 |
| randomized_mse。fit (x, UCS) |
| 训练数据集 |
train_dataset_forecasting = randomized_mse.predict (x_train) |
| 测试数据集 |
test_dataset_forecasting = randomized_mse.predict (x_test) |
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