Roadmap to Master CatBoost

Roadmap to Master CatBoost

0. Why CatBoost?

1. Basic Machine Learning Concepts

  • Bias and Variance
  • Basic Decision Tree: Classification, Regression, Feature Selection, Handling Missing Data

2. Data Encoding Techniques

  • One-Hot Encoding
  • Label Encoding
  • Target Encoding
  • K-Fold Target Encoding

3. Ensemble Methods

  • Random Forest
  • AdaBoost
  • Gradient Boosting: Regression, Classification

4. Regularization Techniques

  • L2 Regression (Ridge)
  • Lasso (L1) Regression
  • Elastic Net Regression

5. Advanced Boosting Algorithms

  • XGBoost
  • CatBoost

6. Model Evaluation Metrics

  • Accuracy
  • Precision
  • Recall
  • F1-Score
  • ROC-AUC

7. Practical Applications

  • Portfolio Construction
  • Portfolio Management
  • Risk Assessment
  • Performance Optimization