Decision Trees and Ensemble learning

Mastering Decision Trees & Ensemble Learning: ML Zoomcamp Week 6 Recap

This week’s ML Zoomcamp was all about Decision Trees and Ensemble Learning, key tools in predictive modeling.

Decision Trees
- Simple & Intuitive: Easy to interpret and visualize.
- Challenges: Prone to overfitting and high variance.

Ensemble Techniques - Bagging (e.g., Random Forests): Reduces variance by building multiple trees in parallel and averaging their predictions. Great for robust, noise-resistant models.
- Boosting (e.g., Gradient Boosting): Builds trees sequentially, each one correcting the previous errors. This boosts accuracy but is more computationally intensive.

Real-World Applications
- Finance: Credit scoring, fraud detection.
- Healthcare: Disease prediction.
- E-commerce: Recommendation engines.

Takeaway: Ensemble methods harness the power of multiple models to deliver more accurate, resilient predictions. Excited to keep building on this foundation in ML Zoomcamp! #MachineLearning #DataScience #MLZoomcamp

Takeaway: Ensemble methods harness the power of multiple models to deliver more accurate, resilient predictions. Excited to keep building on this foundation in ML Zoomcamp! #MachineLearning #DataScience #MLZoomcamp