We have come a long way in the world of Gradient Boosting. If you have followed the whole series, you should have a much better understanding about the theory and practical aspects of the major algorithms in this space. After a grim walk through the math and theory behind these algorithms, I thought it would … Continue reading The Gradient Boosters VII: Battle of the Boosters
The Gradient Boosters IV: LightGBM
XGBoost reigned king for a while, both in accuracy and performance, until a contender rose to the challenge. LightGBM came out from Microsoft Research as a more efficient GBM which was the need of the hour as datasets kept growing in size. LightGBM was faster than XGBoost and in some cases gave higher accuracy as … Continue reading The Gradient Boosters IV: LightGBM
The Gradient Boosters I: The Good Old Gradient Boosting
In 2001, Jerome H. Friedman wrote up a seminal paper - Greedy function approximation: A gradient boosting machine. Little did he know that was going to evolve into a class of methods which threatens Wolpert's No Free Lunch theorem in the tabular world. Gradient Boosting and its cousins(XGBoost and LightGBM) have conquered the world by … Continue reading The Gradient Boosters I: The Good Old Gradient Boosting
Deep & Shallow 