Machine Learning: Random Forest with Python from Scratch - Pros and Cons of Random Forest

The video tutorial discusses the random forest algorithm, highlighting its ability to handle both classification and regression tasks without overfitting. It emphasizes the algorithm's strength in identifying important features using Information Gain. However, it also notes the complexity and slower decision-making process due to multiple decision trees. The tutorial concludes with guidance on when to use random forest, particularly for labeled data in supervised learning scenarios.

In what scenarios should random forest be used for classification or regression?

How does random forest handle a large number of features in a dataset?

What is the role of Information Gain in feature selection within random forest?