MetaDemic Workshop / Free course
Machine Learning Evaluation Beyond a Single Accuracy Score
A sample workshop on selecting baselines, test conditions, robustness checks, and interpretable evaluation measures for machine-learning studies.
Learning objectives
- Choose evaluation measures that match the research question.
- Design meaningful baselines and ablation studies.
- Report uncertainty, failure modes, and practical limitations.
Before you begin
- Basic familiarity with supervised machine learning.
Instructor: Metademic Workshop Team — Sample
Curriculum
Learn one step at a time
01 / What should the metric measure?
Translate the research claim into measurable outcomes and identify where aggregate metrics can mislead.
02 / Baselines and ablations
Construct comparisons that isolate what a proposed method actually contributes.
03 / Robustness and failure analysis
Examine performance across changed conditions and organize failure cases into useful evidence.
Supporting material