Gradient Boosting
Ahh, gradient boosting. In addition to having a totally kickass name, this family of machine learning algorithms is currently among the best known approaches for prediction problems on structured data. This series of posts strives to give a comprehensive understanding of gradient boosting by providing intuitive mathematical explanations, from-scratch implementations of key algorithms, and examples of how to apply modern gradient boosting libraries to solve practical data science problems.
I recommend reading through the series in order, since concepts tend to build on earlier ideas.
Decision Tree from Scratch
A detailed walkthrough of my from-scratch decision tree implementation in python.
XGBoost Explained
In-depth explanation and mathematical derivation of the XGBoost algorithm
XGBoost from Scratch
A walkthrough of my from-scratch python implementation of XGBoost.
XGBoost for Regression in Python
A step-bystep tutorial on regression with XGBoost in python using sklearn and the xgboost library
Gradient Boosting Multi-Class Classification from Scratch
How to implement multi-class classification for gradient boosting from scratch in python