title: Ensemble two models with python
body: <p>I have regression task and I am predicting here with linear regression and random-forest models. Need some hints or code example how to ensemble them (averaging already done). Here are my model realizations with python:</p>
  
  <pre><code>np.random.seed(42)
  mask = np.random.rand(happiness2.shape[0]) &lt;= 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))))
  </code></pre>