After the short introduction to ensemble learning in the previous section, let's start with a warm-up exercise and implement a simple ensemble classifier for majority voting in Python. Although the following algorithm also generalizes to multi-class settings via plurality voting, we will use the term majority voting for simplicity as is also often done in literature.
The algorithm that we are going to implement will allow us to combine different classification algorithms associated with individual weights for confidence. Our goal is to build a stronger meta-classifier that balances out the individual classifiers' weaknesses on a particular dataset. In more precise mathematical terms, we can write the weighted majority vote as follows:
Here, 
is a weight associated with a base classifier, 
, 
is the predicted class label of the ensemble, 
(Greek chi) is the characteristic function 
, and A is the set of unique class labels. For equal weights, we...