Tagged “feature-engineering”

3 posts.

All Model Architecture23 Training & Alignment23 Inference & Serving24 Agents & Orchestration13 Reasoning & Evaluation28 Safety, Security & Governance9 Platforms & Practice22

Reasoning & Evaluation 24 min

Inside Gradient-Boosted Trees: The Engineering That Made XGBoost, LightGBM and CatBoost Win Tabular ML

XGBoost, LightGBM and CatBoost minimise the same objective with the same kind of tree. What separates them is bookkeeping: XGBoost turned two sums of derivatives into a split score, LightGBM made those sums cheap, and CatBoost ma…

trees-and-ensembles gradient-boosting xgboost tabular ∑ ◫

Platforms & Practice 25 min

The Controls That Make It Worse: Colliders, Mediators and Why Adjusting for Everything Is Wrong

Open surgery beat percutaneous nephrolithotomy on small kidney stones, 93% to 87%. It beat it on large stones too, 73% to 69%. Pooled across both, it lost, 78% to 83%. The arithmetic is correct in all three statements, and no amo…

causal-inference statistics confounding dags ∑ ◫

Platforms & Practice 24 min

The Leak in Every Training Set: Feature Stores, Point-in-Time Joins, and the Train-Serve Contract

A fraud model can score perfect recall offline and block nothing in production, because its training join looked a few hours into the future. Feature stores exist to enforce one contract: a training row may only see what the serv…

feature-stores mlops feature-engineering data ∑ ◫