Hands-On Ensemble Learning with Python

Stacking

Moving on to more complex ensembles, we will utilize stacking to combine basic regressors more efficiently. Using StackingRegressor from Chapter 4, Stacking, we will try to combine the same algorithms as we did with voting. First, we modify the predict function of our ensemble (to allow for single-instance prediction) as follows:

 # Generates the predictions def predict(self, x_data):# Create the predictions matrix predictions = np.zeros((len(x_data), len(self.base_learners)))names = list(self.base_learners.keys())# For each base learner for i in range(len(self.base_learners)): name = names[i] learner = self.base_learners[name]# Store the predictions in a column preds = learner.predict(x_data) predictions[:,i] = preds# Take the row-average ...