# A Machine Learning Project Checklist — Machine Learning Basics

Source: https://www.skillbyai.com/en/machine-learning/p-check

> Review any model with these questions.

## Before you trust a result

Is the question precise, with a metric tied to real costs? Is there a baseline? Was the test set split off first and used only once? Is preprocessing inside a pipeline? Have you checked for leakage and suspicious features? Are results reported with cross-validation spread? Are classification thresholds chosen on validation data? Have you looked at errors and per-group performance? Is the model saved with its data version and metrics? Is there a monitoring and retraining plan?

## The checklist

Use it in reviews.

```text
[ ] question + metric linked to real costs
[ ] baseline (dummy model / current rule)
[ ] test set split first, used once
[ ] preprocessing in a pipeline
[ ] leakage check: every feature available at prediction time?
[ ] CV mean and spread reported
[ ] threshold chosen on validation data
[ ] error analysis + per-group results
[ ] model saved with data version and metrics
[ ] monitoring + retraining plan
```

## Explain it to a non-expert

If you cannot explain what the model predicts, how well, and when it fails in plain words, it is not ready.

**Quiz:** Which item belongs on an ML project checklist?

- [ ] Skip the baseline
- [ ] Tune on the test set
- [x] Split off the test set first and use it only once
- [ ] Fit scaling on all data before splitting

*Answer:* Split off the test set first and use it only once. Honest evaluation underpins everything else.