Abstract: In recent years, the demand for efficient and scalable machine learning algorithms has surged. Bagging (Bootstrap Aggregating) stands out as a widely used ensemble technique that combines ...

Let's assume we use a decision tree algorithm as base classifier for all three: boosting, bagging, and (obviously :)) the random forest. Why and when do we want to use any of these? Given a fixed-size ...

This project seeks to explore performance of decision trees, random trees, and bagging models built from scratch. The testlearners.py file is not my own, but was used to evaluate the code for my tree ...