Model Selection & Boosting | Machine Learning

Model Selection & Boosting: Model Selection is the undertaking of choosing a statistical model from an arrangement of candidate models, given information. In the least difficult cases, a prior arrangement of information is considered.

Boosting is a machine learning ensemble meta-algorithm for essentially lessening inclination, and furthermore changes in supervised learning, and a group of machine learning algorithms which change over weak learners to strong ones.

Types of Boosting Algorithms are:

1.    AdaBoost (Adaptive Boosting)

2.    Gradient Tree Boosting

3.    XGBoost