An Animated Guide to Bagging and Boosting in Machine Learning

A step-by-step explanation.

Many folks often struggle to understand the core essence of bagging and boosting.

I prepared this animation, which depicts what goes under the hood:

In a gist, an ensemble combines multiple models to build a more powerful model.

They are fundamentally built on the idea that by aggregating the predictions of multiple models, the weaknesses of individual models can be mitigated.

Whenever I wish to intuitively illustrate their immense power, I use the following image:

Single model vs. Ensemble

Ensembles are primarily built using two different strategies:

1. Bagging

2. Boosting

1) Bagging (short for Bootstrapped Aggregation):

Bagging animation

• creates different subsets of data with replacement (this is called bootstrapping)

  • Why “with replacement and why bagging is extremely effective?”

  • I have a full article on this if you are interested in learning more: Why Bagging is So Ridiculously Effective At Variance Reduction?

• trains one model per subset

• aggregates all predictions to get the final prediction

Some common models that leverage bagging are:

• Random Forests

• Extra Trees

2) Boosting:

Boosting animation

• is an iterative training process

• the subsequent model puts more focus on misclassified samples from the previous model

• the final prediction is a weighted combination of all predictions

Some common models that leverage boosting are:

• XGBoost,

• AdaBoost, etc.

Overall, ensemble models significantly boost the predictive performance compared to using a single model.

They tend to be more robust, generalize better to unseen data, and are less prone to overfitting.

👉 Over to you: What are some challenges/limitations of using ensembles? Let’s discuss it today :)

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