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This small teaching project demonstrates how to run stratified cross-validation correctly for a supervised classification problem and how to retain a held-out test for final evaluation. It emphasises: (1) preparing the data with awareness of class labels before choosing folds, (2) building a preprocessing-and-model pipeline and running cross-validation with a stratified splitter, (3) reporting per-fold scores and their spread, and (4) creating a fold report that exposes row assignments and preprocessing boundaries.
The data are synthetic and generated with scikit-learn’s make_classification inside the code. This is a controlled, small dataset that imitates a multi-feature binary-class classification task for teaching purposes.
Run the module directly: python project.py. The script prints a JSON object with cross-validation metrics, holdout evaluation and a fold report.
Run pytest in the project directory: pytest -q. The tests check outputs are present, metrics lie in valid ranges, a conservative metric floor is met and that results are deterministic for a fixed seed.
Measured by running python project.py (seed 42) on 2026-10-23; the tests in test_project.py assert the floor.
| Metric | Value |
|---|---|
| cv_mean_accuracy | 0.7250 |
| cv_std_accuracy | 0.0217 |
| holdout_accuracy | 0.7688 |
| n_folds | 5 |
Also returned (see python project.py output): fold_report (5 items).
The example uses synthetic data produced with make_classification and a logistic regression model. Synthetic data cannot capture all complexities of real-world collections (for example, temporal dependencies, per-subject grouping or measurement shifts). The demonstration focuses on illustrating cross-validation mechanics rather than producing a deployable model. Also, the pipeline uses standard scaling and a simple classifier; other preprocessing, feature selection or model choices may be needed in practice.
Possible extensions include: use grouped cross-validation (GroupKFold) when data contain repeated entities, use time-aware split strategies for temporal data, evaluate alternative models and calibrations, and persist fold-level preprocessing artefacts to inspect leakage risks.
Full case study, build steps and evaluation checks: Cross-validation implementation on networkershome.com
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