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_贝叶斯_优化是一种优化算法，用于寻找最优的超参数组合。在xgboosting _回归预测_中，我们可以使用 _贝叶斯_优化来优化模型的超参数，例如树的最大深度、学习率、正则化参数等。 以下是使用 _贝叶斯_优化xgboosting _回归预测_的步骤： 1. 导入必要的库和数据集 _python_ import xgboost as xgb from bayes_opt import BayesianOptimization from sklearn.datasets import load_boston from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split data = load_boston() X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42) 2. 定义模型训练函数 _python_ def xgb_cv(max_depth, learning_rate, n_estimators, gamma, min_child_weight, subsample, colsample_bytree): params = { "objective": "reg:squarederror", "eval_metric": "rmse", "max_depth": int(round(max_depth)), "learning_rate": learning_rate, "n_estimators": int(round(n_estimators)), "gamma": gamma, "min_child_weight": min_child_weight, "subsample": subsample, "colsample_bytree": colsample_bytree, "seed": 42 } dtrain = xgb.DMatrix(X_train, label=y_train) cv_result = xgb.cv(params, dtrain, num_boost_round=1000, nfold=5, early_stopping_rounds=50, verbose_eval=None, seed=42) return -cv_result["test-rmse-mean"].iloc[-1] 3. 定义超参数范围 _python_ pbounds = { "max_depth": (3, 10), "learning_rate": (0.01, 0.3), "n_estimators": (100, 1000), "gamma": (0, 5), "min_child_weight": (1, 10), "subsample": (0.5, 1), "colsample_bytree": (0.5, 1) } 4. 运行 _贝叶斯_优化 _python_ optimizer = BayesianOptimization(f=xgb_cv, pbounds=pbounds, random_state=42) optimizer.maximize(init_points=10, n_iter=30) 5. 训练最优的模型并进行预测 _python_ best_params = optimizer.max["params"] best_params["max_depth"] = int(round(best_params["max_depth"])) best_params["n_estimators"] = int(round(best_params["n_estimators"])) model = xgb.XGBRegressor(**best_params) model.fit(X_train, y_train) y_pred = model.predict(X_test) mse = mean_squared_error(y_test, y_pred) print("MSE: {:.2f}".format(mse)) 通过 _贝叶斯_优化，我们可以得到最优的超参数组合，从而提高模型的预测性能。](https://wenku.csdn.net/answer/5fcc1ee6315e42a8a6928f6dddd61691)
Original: https://blog.csdn.net/qq_45083082/article/details/124279580
Author: 益益和仔仔
Title: 贝叶斯回归预测
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