What Are Ensembles in Machine Learning? An Introduction to Ensemble Learning
What is Ensemble Learning?
The term ensemble means "a collection of things or a group of things."
In the context of machine learning, ensemble learning refers to the technique of combining multiple models to create a stronger, more accurate, and robust predictive system than any single model could achieve alone.
Real life scenario.....
To understand ensemble learning better, think about how different musicians with unique instruments come together to compose a beautiful symphony(Fig:- 1).
Similarly, multiple machine learning models with diverse strengths are combined to produce better predictions(Fig:- 2).
Key Concepts of Ensembles
Base Models (M1, M2, M3, ... Mn):
These are the individual models that form the building blocks of the ensemble. The unique contributions of each model are combined to create a powerful and accurate predictive system.Why Combine Models?
No single model is perfect. By combining the predictions of multiple models, we can reduce errors, improve accuracy, and create a robust model that generalizes well on unseen data.
Types of Ensemble Learning Techniques:-
Bagging (Bootstrap Aggregating):
Aim: Reduce variance.
Process: Train multiple models independently on random subsets of the data (with replacement) and average their predictions.
Example: Random Forest.
Boosting:
Aim: Reduce bias.
Process: Train models sequentially, where each model focuses on correcting the errors of the previous one.
Example: Gradient Boosting Machines, AdaBoost.
Stacking:
Aim: Combine the strengths of multiple models.
Process: Train different models and use their predictions as inputs for a meta-model that makes the final prediction.
Cascading:
Aim: Build a hierarchical decision-making system.
Process: Pass data through a sequence of models, where each model handles specific cases or features.
By using these model we can build very high and powerful models.
In the next blog, we will explore each type of ensemble model in detail, discussing their working principles, advantages, and practical applications.
Bagging (Bootstrap Aggregating):
Aim: Reduce variance.
Process: Train multiple models independently on random subsets of the data (with replacement) and average their predictions.
Example: Random Forest.
Boosting:
Aim: Reduce bias.
Process: Train models sequentially, where each model focuses on correcting the errors of the previous one.
Example: Gradient Boosting Machines, AdaBoost.
Stacking:
Aim: Combine the strengths of multiple models.
Process: Train different models and use their predictions as inputs for a meta-model that makes the final prediction.
Cascading:
Aim: Build a hierarchical decision-making system.
Process: Pass data through a sequence of models, where each model handles specific cases or features.
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