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
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