用python集成兩個模型

Question

我有回歸任務，我在這里用線性回歸和隨機森林模型進行預測。 需要一些提示或代碼示例如何組合它們（平均已經完成）。 這是我使用 python 實現的模型：

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，您可以在驗證集上評估每個模型（線性回歸和隨機森林）並找出錯誤（例如 MSE）。 然后，根據這個誤差對每個模型進行加權，然后在預測時使用這個權重。

您也可以使用眼鏡蛇合奏方法（由 Guedj 等人開發） https://modal.lille.inria.fr/pycobra/