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What is Bagging vs Random Forest Explained about?

Bagging is an ensemble technique that reduces variance and overfitting by training multiple models on bootstrap samples and aggregating their outputs. Random Forest builds on bagging by incorporating random feature selection at each split, resulting in a diverse set of trees and improved performance. Both techniques enhance model stability and accuracy, with Random Forest being particularly effective with large feature sets.

Author

BalaMadhusudanaReddy

Language

EN