Ensemble Learning: Implement Boosting, Bagging, and Stacking

Learn how to enhance the robustness of your Machine Learning algorithms with Ensemble Learning

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Ensemble learning is a powerful machine learning technique that combines multiple individual models to produce a more accurate and robust prediction. Ensemble learning techniques are especially useful when dealing with complex and non-linear problems that traditional single models cannot handle effectively.

There are three main ensemble techniques: Boosting, Bagging, and Stacking. Each technique has its own strengths and weaknesses and can be used in different applications.

Let’s see how we can implement each of these techniques in Python

Boosting

Boosting is a sequential ensemble technique that trains weak models iteratively and adjusts their weights to give more emphasis to instances that are misclassified. The final prediction is a weighted combination of all weak models.

AdaBoost (Adaptive Boosting) is the most popular boosting algorithm. Here’s a code snippet in Python to implement AdaBoost:

from sklearn.ensemble import AdaBoostClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import…