以下是几种不同场景下使用 Python 实现 XGBoost 进行预测的代码示例： ### 简单的 XGBoost 回归树预测示例 ```python import xgboost as xgb import numpy as np from sklearn.datasets import make_regression from sklearn.model_selection import train_test_split # 生成示例数据 X, y = make_regression(n_samples=1000, n_features=10, noise=0.1, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 创建 XGBoost 回归模型 xgb_model = xgb.XGBRegressor(objective='reg:squarederror', colsample_bytree=0.3, learning_rate=0.1, max_depth=5, alpha=10, n_estimators=100) # 训练模型 xgb_model.fit(X_train, y_train) # 进行预测 y_pred = xgb_model.predict(X_test) print("预测结果:", y_pred) ``` ### CNN - XGBoost 结合的时间序列预测示例 ```python import xgboost as xgb import numpy as np from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv1D, Flatten # 假设已有 CNN 模型 # 简单构建一个 CNN 模型示例 cnn_model = Sequential() cnn_model.add(Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=(10, 1))) cnn_model.add(Flatten()) cnn_model.compile(optimizer='adam', loss='mse') # 生成示例数据 X = np.random.rand(100, 10, 1) y = np.random.rand(100) X_reshaped = X.reshape(-1, 10, 1) # 从 CNN 模型提取特征 cnn_features = cnn_model.predict(X_reshaped) # XGBoost 模型 xg_model = xgb.XGBRegressor(objective='reg:squarederror', colsample_bytree=0.3, learning_rate=0.1, max_depth=5, alpha=10, n_estimators=100) xg_model.fit(cnn_features, y) # 预测 y_pred = xg_model.predict(cnn_features) print("预测结果:", y_pred) ``` ### LSTM - XGBoost 结合的多输入单输出回归预测示例 ```python import xgboost as xgb import numpy as np # 生成示例数据 train_features = np.random.rand(100, 10) train_targets = np.random.rand(100) # 构造 DMatrix 数据格式，提高计算效率 dtrain = xgb.DMatrix(train_features, label=train_targets) # 参数设置 params = { 'objective': 'reg:squarederror', # 目标函数为平方误差 'eval_metric': 'rmse', # 评估指标为均方根误差 'eta': 0.1, # 学习率 'subsample': 0.8, # 随机采样比例，减少过拟合 'colsample_bytree': 0.8, # 构造树时列采样比例 'seed': 42 # 随机种子，保证结果复现 } # 训练 100 轮提升树 model = xgb.train(params, dtrain, num_boost_round=100) # 生成测试数据 test_features = np.random.rand(10, 10) dtest = xgb.DMatrix(test_features) # 进行预测 y_pred = model.predict(dtest) print("预测结果:", y_pred) ```