Decision Trees with Extreme Boosted Trees

Requirements Clarification & Assessment

1. Understanding the Context:

   • Objective: To compare the mathematical foundations of decision trees and extreme boosted trees (XGBoost).
   • Scope: Focus on the mathematical differences, specifically how each method approaches learning and optimization.

2. Key Concepts to Address:

   • Decision Trees:
     • Structure and formation.
     • Splitting criteria (e.g., Gini impurity, entropy).
     • Overfitting and variance issues.
   • Extreme Boosted Trees (XGBoost):
     • Ensemble learning technique.
     • Boosting algorithm and sequential training.
     • Loss function and regularization.

3. Target Audience:

   • Interviewers evaluating the candidate's understanding of machine learning algorithms.
   • Data scientists familiar with basic and advanced machine learning concepts.

4. Expected Depth of Explanation:

   • Mathematical formulation and underlying principles.
   • Practical implications of differences.