ml-evaluation-framework

To ensure machine learning evaluation is statistically valid, you must run experiments across a minimum of three random seeds, report mean and standard deviation, and use confidence intervals or formal significance tests before claiming any model improvements.

A rigorous classification evaluation requires a full metric suite rather than a single score, alongside user-specified metrics, variance estimation across multiple seeds, and fair baseline comparisons computed on identical data splits.

Designing a valid ablation study requires isolating novel components, comparing them against fair baselines using identical data preprocessing, and validating any reported performance differences with confidence intervals or statistical significance tests.

Reporting benchmark improvements without multiple random seeds is invalid. You must execute at least three seeds, calculate variance, and apply formal significance testing or confidence intervals to justify any claimed performance gains.

Statistical significance testing in ML benchmarking is used to verify that performance differences between models are genuine and not random noise, requiring fair baseline reproductions on identical data splits and multi-seed variance estimation.

You should use confidence intervals instead of just mean accuracy whenever preparing benchmark reports or publishing model improvements, as they quantify the uncertainty of your estimates across multiple random seeds.