Comparing Ensemble Methods

Comparing ensemble methods helps us recognize the relative strengths and weaknesses of approaches like bagging, boosting, and stacking (which we’ll look at below). Each method has unique characteristics—bagging reduces variance, boosting reduces bias, and stacking leverages multiple algorithms to enhance predictive performance. Through comparative experiments on various datasets, we can determine which ensemble strategy works best for specific problems. This recipe will allow you to compare different methods for ensemble training methods.

Getting ready

We'll demonstrate comparing different ensemble methods using scikit-learn with a classification dataset.

1. Load the libraries:

   import numpy as np
   import pandas as pd
   from sklearn.datasets import load_iris
   from sklearn.model_selection import train_test_split
   from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
   from sklearn.ensemble import StackingClassifier
   from sklearn...