Visiting: Categorical Features and Encoding in Decision Trees

xgboost: “Hi I’m Gamma. What can I do for you?” — and the tuning of regularization

Laurae: This post is about tuning the regularization in the tree-based xgboost (Maximum Depth, Minimum Child Weight, Gamma). It also explains what are these regularization parameters in…

Destroying the Myth of “number of threads = number of physical cores”

tl;dr: max performance => number of threads = number of logical/virtual cores

xgboost GPU performance on low-end GPU vs high-end CPU

Ensembles of tree-based models: why correlated features do not trip them — and why NA matters

Laurae: This post is about decision tree ensembles (ex: Random Forests, Extremely Randomized Trees, Extreme Gradient Boosting…) and correlated features. It explains why…

Let me learn the learning rate (eta) in xgboost! (or in anything using Gradient Descent optimization)

Laurae: This post is about choosing the learning rate in an optimization task (or in a supervised machine learning model, like xgboost for this example). The…

Getting the most of xgboost and LightGBM speed: Compiler, CPU pinning