我有一个回归任务，这里我使用线性回归和随机森林模型进行预测。需要一些提示或代码示例来集成它们（平均已经完成）。这是我在Python中实现的模型代码：

np.random.seed(42)mask = np.random.rand(happiness2.shape[0]) <= 0.7print('Train set shape {0}, test set shape {1}'.format(happiness2[mask].shape, happiness2[~mask].shape))from sklearn.linear_model import LinearRegressionlr = LinearRegression()lr.fit(happiness22[mask].drop(['Country', 'Happiness_Score_2017',                               'Happiness_Score_2018','Happiness_Score_2019'], axis=1).fillna(0),        happiness22[mask]['Happiness_Score_2019'] )pred = lr.predict(happiness22[~mask].drop(['Country', 'Happiness_Score_2017',                               'Happiness_Score_2018','Happiness_Score_2019'], axis=1).fillna(0)) print('RMSE = {0:.04f}'.format(np.sqrt(np.mean((pred - happiness22[~mask]['Happiness_Score_2019'])**2)))) from sklearn.ensemble import RandomForestRegressorrf = RandomForestRegressor(n_estimators=100)rf.fit(happiness22[mask].drop(['Country', 'Happiness_Score_2017',                               'Happiness_Score_2018','Happiness_Score_2019'], axis=1).fillna(0),        happiness22[mask]['Happiness_Score_2019'] )pred3 = rf.predict(happiness22[~mask].drop(['Country', 'Happiness_Score_2017',                               'Happiness_Score_2018','Happiness_Score_2019'], axis=1).fillna(0))print('RMSE = {0:.04f}'.format(np.sqrt(np.mean((pred3 - happiness22[~mask]['Happiness_Score_2019'])**2))))avepred=(pred+pred3)/2print('RMSE = {0:.04f}'.format(np.sqrt(np.mean((avepred - happiness22[~mask]['Happiness_Score_2019'])**2))))

回答：

首先，您可以在验证集上评估每个模型（线性回归和随机森林），并获取错误（例如均方误差）。然后，根据这个错误为每个模型分配权重，并在预测时使用这些权重。

您还可以使用由Guedj等人开发的cobra集成方法 https://modal.lille.inria.fr/pycobra/