generate-test-design

To create a CRISP-DM test design, define your train/validation/test splitting strategies, specify evaluation metrics, and establish baselines to ensure reproducible model evaluation across experiments.

A rigorous splitting strategy requires clear train/validation/test splits, defined cross-validation approaches, and reproducibility criteria to ensure consistent model evaluation across experimental runs.

Document experiment tracking and baselines by creating a Jupyter notebook at notebooks/4.2-test-design.ipynb and a summary doc at docs/crisp-dm/4-modeling/4.2-test-design.md to record results and artifacts.

Yes, this test design approach works for reproducible modeling projects by defining concrete requirements for artifacts, documentation paths, and reproducibility criteria across all data splits.

Defining train, validation, and test splits before model building establishes the experimental framework, ensuring rigorous evaluation metrics and experiment tracking prevent biased model assessment.

Without experiment tracking, manual test designs lack reproducible evaluation across splits, clear baselines, and artifact documentation, compromising the rigor of the modeling project.