Ensemble learning is a method that combines multiple machine learning models to create more powerful models.
There are many reasons for a model to be different. Few reasons are:
- Different Population
- Different Hypothesis
- Different modeling techniques
When working with the model’s training and testing data, we will experience an error. This error might be bias, variance, and irreducible error.
Now the model should always have a balance between bias and variance, which we call a bias-variance trade-off.
This ensemble learning is a way to perform this trade-off.
There are many ensemble techniques available but when aggregating multiple models there are two general methods:
- Bagging, a native method: take the training set and generate new training sets off of it.
- Boosting, a more elegant method: similar to bagging, boosting is used to optimize the best weighting scheme for a training set.