Tagged “machine-learning”

5 posts.

All Model Architecture19 Training & Alignment22 Inference & Serving18 Agents & Orchestration11 Reasoning & Evaluation26 Safety, Security & Governance7 Platforms & Practice20

Reasoning & Evaluation 24 min

Double Machine Learning: How to Use Flexible Models for Causal Estimates Without Inheriting Their Bias

Plug a random forest into a causal regression and the confidence interval tightens around the wrong number, because regularisation bias shrinks more slowly than the standard error. Double machine learning fixes this with two devi…

observational-causal-methods causal-inference confounding estimation ∑ ◫

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 ∑ ◫

Reasoning & Evaluation 24 min

The Curse of Dimensionality: Why Distances Stop Meaning Anything, and Why Learning Works Anyway

Scatter 1,000 random points in a 1,000-dimensional cube and the farthest one from a query is only about 12% farther away than the nearest. By that arithmetic nearest-neighbour search should be meaningless, and yet every vector da…

supervised-classical learning-theory embeddings vector-search ∑ ◫

Reasoning & Evaluation 24 min

What a Feature Attribution Can and Cannot Tell You: SHAP, LIME and the Explanation Gap

Add a column the model never reads and SHAP can hand it more than a quarter of the credit for a decision. That is not a library bug: a Shapley attribution answers a question you chose, often without noticing, and a 2024 PNAS resu…

transparency-and-documentation interpretability responsible-ai causal-inference ∑ ◫

Reasoning & Evaluation 3 min

What the bake-off taught us: classical ML is not dead, it is just under-attended

We pitted twelve sklearn algorithms head-to-head on a tabular dataset. The winner was not the most expensive one. It was not the most modern one. It was the one whose assumptions matched the data.

machine-learning tabular benchmarks evaluation