leakage

A bad split silently inflates every metric you'll ever report, and the bugs are subtle: leaked groups, shuffled time, preprocessing fit on everything. Here is the split done right.

Tabular ML is won on features, and this question checks whether you navigate the practical traps: target leakage in encodings, informative missingness, and fitting transforms on the wrong data. The signal is leakage-safe preprocessing. Here is the toolkit.

Time series breaks the usual ML assumptions: data is ordered and correlated, so random cross-validation silently leaks the future and inflates your score. What matters is decomposition, point-in-time features, and time-aware validation.

Cross-validation yields a reliable performance estimate, but the wrong scheme leaks data and misleads. What matters is matching the CV scheme to the data (stratified, grouped, time-series) and fitting preprocessing inside the fold.

The most frequent way SQL leaks the future into a training set is a sloppy join to a feature table. The fix is point-in-time correctness, implemented as an as-of join. Here is how to write it.

Defining the label is half the modeling problem, and the SQL conceals two leakage traps: reaching into the future for features, and a label window not yet fully observed. Here is the correct query.