【发布时间】：2020-04-07 11:32:15
【问题描述】：
我正在使用带有 XGBRegressor 的 scikit-learn 管道。 管道运行良好，没有任何错误。当我使用这条管道进行预测时，我会多次预测相同的数据，有时预测值会随机出现 0.5，而正常的预测范围是 (1000-10,000)
例如： (1258.2,1258.2,1258.2,1258.2,1258.2,1258.2,0.5,1258.2,1258.2,1258.2,1258.2)
- 输入数据完全相同
-
环境一样
numeric_transformer = Pipeline(steps=[ ('imputer', SimpleImputer(strategy='mean')), ('scaler', StandardScaler())]) categorical_transformer = Pipeline(steps=[ ('imputer', SimpleImputer(strategy='constant', fill_value='missing')), ('onehot', OneHotEncoder(handle_unknown='ignore')) ]) numeric_features = X.select_dtypes( include=['int64', 'float64']).columns categorical_features = X.select_dtypes( include=['object']).columns preprocessor = ColumnTransformer( transformers=[ ('num', numeric_transformer, numeric_features), ('cat', categorical_transformer, categorical_features)]) # Number of trees n_estimators = [int(x) for x in np.linspace(start=50, stop=1000, num=10)] # Maximum number of levels in tree max_depth = [int(x) for x in np.linspace(1, 32, 32, endpoint=True)] # Booster booster = ['gbtree', 'gblinear', 'dart'] # selecting gamma gamma = [i / 10.0 for i in range(0, 5)] # Learning rate learning_rate = np.linspace(0.01, 0.2, 15) # Evaluation metric # eval_metric = ['rmse','mae'] # regularization reg_alpha = [1e-5, 1e-2, 0.1, 1, 100] reg_lambda = [1e-5, 1e-2, 0.1, 1, 100] # Min chile weight min_child_weight = list(range(1, 6, 2)) # Samples subsample = [i / 10.0 for i in range(6, 10)] colsample_bytree = [i / 10.0 for i in range(6, 10)] # Create the random grid random_grid = {'n_estimators': n_estimators, 'max_depth': max_depth, 'booster': booster, 'gamma': gamma, 'learning_rate': learning_rate, # 'eval_metric' : eval_metric, 'reg_alpha': reg_alpha, 'reg_lambda': reg_lambda, 'min_child_weight': min_child_weight, 'subsample': subsample, 'colsample_bytree': colsample_bytree } # Use the random grid to search for best hyperparameters # First create the base model to tune rf = xgboost.XGBRegressor(objective='reg:squarederror', n_jobs=4) # Random search of parameters, using 3 fold cross validation, # search across 100 different combinations, and use all available cores rf_random = RandomizedSearchCV(estimator=rf, param_distributions=random_grid, n_iter=100, cv=3, verbose=0, random_state=42, n_jobs=4) pipe = Pipeline(steps=[('preprocessor', preprocessor), ('regressor', rf_random)]) pipe.fit(X, y)
可能是什么问题？
【问题讨论】：
标签： python machine-learning scikit-learn xgboost