| name | feature-engineering-and-modeling |
| description | Engineer features and fit, select, and honestly evaluate classical models on tabular data: build leakage-aware features, set a baseline, choose between regression / trees / gradient boosting, build a leakage-safe cross-validation harness with every transform fit inside the fold, and pick the metric from the decision and its cost structure. |
Feature Engineering & Modeling
Baseline first
Set a dumb baseline (mean / majority class / simple linear) — it's the bar every model must clear. A model that barely beats it is a finding about the data, not a win. Report the lift, not just the absolute score.
Engineer features leakage-aware
Encodings, interactions, transforms, binning, time/group aggregates — but defer every fit-based transform (scaling, imputing, target encoding) to inside the cross-validation fold. Computing them on the full data before the split leaks the test set.
Classical before deep
For tabular data: regularized regression and shallow trees for interpretability; gradient boosting (XGBoost / LightGBM) as the strong default. Reach for deep learning / AutoML only when it earns its complexity and operational cost.
Build a leakage-safe CV harness
Split before any fit. Wrap transforms in a Pipeline/ColumnTransformer so they stay inside the fold by construction. Use stratified folds for imbalance, GroupKFold for grouped data, TimeSeriesSplit for temporal data. Tuning hyperparameters? Nested CV, and the test set is touched once.
Choose the metric from the decision
The wrong metric makes a bad model look good — accuracy on imbalanced classes, R² on a ranking problem, a symmetric loss for asymmetric cost. Derive the metric from the cost structure (PR-AUC, recall-at-precision, cost-weighted loss, calibration) and justify it.
Report honestly
Report the CV spread, not a single-split point estimate; the lift over baseline; and the leakage check. Feature importance is association, not causation — route causal and significance claims to applied-statistics.
Output
A model experiment record: the features, the baseline, the model, the CV scheme, the metric and its justification, the leakage check, and the honest score with its spread. Hand productionizing to ml-engineering and significance to applied-statistics.
