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Topic 3.9 of Data Collection and Analysis Methods

Experimental Design for ML Research

The choices that determine whether an ML experiment's results are trustworthy — dataset splits, cross-validation, ablation studies, baselines, and statistical significance — and the reproducibility practices that let another researcher verify them.

~30 min total·4 quadrants of structured content

By the end of this topic, you will

Explain why a test set must be invisible during model development and what happens to results when it is not
Choose between a single train/val/test split and k-fold cross-validation given the size and distribution of a dataset
Design an ablation study that isolates the contribution of a single component in a proposed system
State what a baseline must satisfy to make a comparison fair
Apply a statistical significance test to compare two ML classifiers on the same dataset

Quadrant 1 · e-Tutorial

Video lectures and walkthroughs

Quadrant 2 · e-Content

Articles and case studies

research-methodology

Experimental Design for ML Research

Reporting 98% accuracy on a test set that the model saw during development is not a result — it is a measurement error. ML research has its own experimental design discipline: how you split data, choose baselines, structure ablations, and report variance determines whether your numbers mean anything.

15 min read

Quadrant 3 · Web Resources

Downloadable material and curated external links

Web resources coming soon.

Quadrant 4 · Self-Assessment

Test your knowledge — earn a certificate on first pass

Assessment coming soon.