#cross-validation
2 articles
01
·Machine Learning Basics·★ MEMBER·10 min read
Data Leakage and Experiment Hygiene — When the Score Is Too Good, Suspect It
When a model scores better than you expected, that is not good news yet. This article splits data leakage into five concrete shapes — outcome-derived columns, preprocessing before the split, time running backwards, duplicates and groups, and a worn-out test set — then covers the detection moves that actually find them and the experiment records that let you dissect the day the number jumped.
02
·Machine Learning Basics·★ MEMBER·8 min read
Overfitting and Evaluation Design — Be Suspicious of 99% Accuracy
Why a model that fits its training data perfectly falls apart in production: the division of labour between train, validation and test, cross-validation, the data leakage that ruins more projects than any modelling mistake, and how accuracy lies on imbalanced data.