Practical Data Science using Python - Random Forest Steps Pruning and Optimization

The video tutorial explains decision trees and random forests, focusing on their structure, hyperparameters, and the bagging process. It highlights the importance of hyperparameters like Gini index and entropy in optimizing models and preventing overfitting. The tutorial also covers the out of bag score for model validation and the steps to build and use random forests. Additionally, it discusses feature importance and its role in identifying influential features. The tutorial concludes with a practical application of random forests in predicting loan defaults using historical financial data.

What are the potential drawbacks of not constraining a decision tree model?

In what scenarios would you prefer using a random forest over a single decision tree?

What role does the 'n_estimators' parameter play in a random forest model?

How can hyperparameters be optimized for a random forest model?