Difference between Bagging and Boosting and Stacking

Bagging:

Bagging is an ensemble learning technique that combines the benefits of bootstrapping and aggregation to yield a stable model and improve the prediction performance of a machine learning model. It also known as Bootstrap Aggregation.

Boosting:

Boosting is an ensemble learning technique that combines the predictions of weak learners sequentially. Each new model is trained on a weighted training set. We assign weights based on the errors of the previous models in the sequence.

Stacking:

In stacking, the predictions of base models are fed as input to a meta-model. The job of the meta-model is to take the predictions of the base models and make a final prediction: It also known as Stacked Generalization.

Bagging vs Boosting vs Stacking:

Parameters	Bagging	Boosting	Stacking
Partitioning of the data into subsets	Random	Giving mis-classified samples higher preference	Various
Goal to achieve	Minimize variance	Increase predictive force	Both
Methods where it is used	Random subspace	Gradient descent	Blending
Function to combine single models	Weighted average	Weighted majority vote	Logistic regression