用 python 集成两个模型

Question

我有回归任务，我在这里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如何集成它们（平均已经完成）。这是我的模型实现 python:

np.random.seed(42)
mask = np.random.rand(happiness2.shape[0]) <= 0.7

print('Train set shape {0}, test set shape {1}'.format(happiness2[mask].shape, happiness2[~mask].shape))

from sklearn.linear_model import LinearRegression
lr = 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 RandomForestRegressor

rf = 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)/2
print('RMSE = {0:.04f}'.format(np.sqrt(np.mean((avepred - happiness22[~mask]['Happiness_Score_2019'])**2))))

Answer 1

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）并找出错误（例如 MSE）。然后根据这个误差对每个模型进行加权，后面预测的时候就用这个权重。

您也可以使用眼镜蛇集成方法（由 Guedj 等人开发） https://modal.lille.inria.fr/pycobra/

用 python 集成两个模型

Ensemble two models with python

python

machine-learning

linear-regression

random-forest

ensemble-learning