leakage

A wrong split quietly 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 handle the practical traps: target leakage in encodings, missingness that is informative, 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 quietly leaks the future and inflates your score. The signal is decomposition, point-in-time features, and time-aware validation.

Cross-validation gives a reliable performance estimate, but the wrong scheme leaks data and lies. The signal is matching the CV scheme to the data (stratified, grouped, time-series) and fitting preprocessing inside the fold.

The single most common way SQL leaks the future into a training set is a careless join to a feature table. Point-in-time correctness is the fix, and it is an as-of join. Here is how to write it.

Defining the label is half the modeling problem, and the SQL hides two leakage traps: looking into the future for features, and a label window that isn't fully observed yet. Here is the correct query.