ML Experiment Standards

Purpose: every ML job (quick prototypes included) is reproducible, leakage-free, and metric-justified. These are not optional polish; every skipped item typically returns as "the model collapsed in production" or "the result didn't replicate".

1. EDA comes first

Before any model, produce and show: distributions, missingness rates, outliers, target balance, salient correlations. Metric and loss choice depend on this information; a model recommendation without EDA is a guess.

2. Leakage audit

At every split decision, answer explicitly (and write the answer as a code comment): "Does the training set contain indirect information about any test sample?"