In this example, we are going to use the famous Wine dataset (178 13-dimensional samples split into three classes) that is directly available in Scikit-Learn. Unfortunately, it's not so easy to find good and simple datasets for ensemble learning algorithms, as they are normally employed with large and complex sets that require too long a computational time. As the Wine dataset is not particularly complex, the first step is to assess the performances of different classifiers (logistic regression, decision tree, and polynomial SVM) using a k-fold cross-validation:
import numpy as npfrom sklearn.datasets import load_winefrom sklearn.model_selection import cross_val_scorefrom sklearn.linear_model import ...
