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Random Forests
Ensemble Methods
Decision Trees

Can you explain the concept of Random Forests? Describe how a Random Forest model is constructed by combining multiple decision trees. Discuss the key principles behind random feature selection and bootstrap aggregating (bagging) in building a Random Forest. Additionally, highlight the advantages of Random Forests in terms of handling overfitting, feature importance, and their robustness to noisy data. Could you also discuss any limitations or scenarios where Random Forests might not be the best choice among ensemble methods?

machine learning
Senior Level

Random Forest is an ensemble learning method that operates by constructing multiple decision trees during training and outputs the mode of the classes (classification) or the average prediction (regression) of the individual trees.

Construction of a Random...

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