Model Selection and 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. However, the assignment can likewise include the outline of trials with the end goal that the information gathered is appropriate to the problem of model selection. Given candidate models of comparable prescient or illustrative power, the least complex model is well on the way to be the best decision

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. A weak learner is characterized to be a classifier which is just marginally related with the genuine characterization (it can name cases superior to anything irregular speculating). Conversely, a strong learner is a classifier that is subjectively all around connected with the genuine classification.

Types of Boosting Algorithms are:

1.       AdaBoost (Adaptive Boosting)

2.       Gradient Tree Boosting

3.       XGBoost

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