What is Ensemble Learning in Machine Learning| #24 of 28 | Foundations of ML: The Big Picture

let's develop an intuition about what ensemble model or ensamble learning is, what ensamble learning is.

We will clearly understand in this one, we will develop an intuition around it.


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🔹What is Ensemble learning in Machine Learning

Now, this is also a term that you will come across very often in machine learning world. And we will try to understand the intuition around it.

Now, what is ensemble learning, it is actually a type of machine learning model. That's what it is. It's a type of machine learning model that uses multiple ml models to make the final prediction.

So, whatever we have been doing so far, we have been seeing so far your prediction comes from one single ml model. ensemble learning in turn uses a lot of not just multiple a lot of ml models to come up with a final prediction.

Few popular ml or ensemble algorithms is random forests, and gradient boosting very popular ensembles. Right.

So how does this work? What's the general principle behind ensemble learning in general, there are two very popular ensembles, it is not limited to only these two, but these two are very popular ones bagging and boosting.

And bagging here is actually the shot for bootstrap aggregation. The B comes from both bootstrap and aggregation ages he comes from here tagging.

Now, in bagging, what we saw in the previous slide, random forest forest is actually a type of bagging algorithm.

Likewise, gradient boosting is a type of boosting algorithm. Now, what is the fundamental difference between these two?

In bagging you have a data, you have the data set, instead of what we have been seeing so far is from this data set, we will one single ml model, and this is going to give you the prediction.

This is generally how machine learning models work. Here also, finally, we'll get only one prediction. Finally, you will get only one prediction.

But this final prediction actually comes from multiple different predictions of machine learning models.

These are all different different machine learning models. Typically, a lot of machine learning models will be involved here in this representation, we have just five.

But when building, say a random forest, you will typically build say 100, classifiers, 100 different models or 500 models, or even 1000 models.

These are all common numbers, right? going and building 1000 different decision trees, random forests is actually made up of decision trees, those are different decision trees.

And then using those predictions to come up with a final prediction is not uncommon.



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