machine-learning evaluation statistics
Definition
Evaluation (Machine Learning)
Machine learning evaluation is the systematic process of quantifying the performance and reliability of a learned model. It involves measuring how effectively a model’s predictions align with ground-truth labels across various dataset splits to ensure the model has successfully achieved generalisation.
The Evaluation Pipeline
A robust evaluation strategy typically follows a standardised sequence of operations:
Metric Calculation: The selection of a task-appropriate metric (e.g., Accuracy, F1-Score, or RMSE) to quantify the model’s error rate on the test set.
Baseline Comparison: Establishing a Baseline (e.g., a constant or random predictor) to verify that the model provides significant information gain over trivial solutions.
Statistical Significance: Utilising tests such as the Student-t test to determine if the performance differences between models are statistically significant and not the result of random variance in the data sampling.
Observations on Model Verification
Shortcut Learning (Clever Hans Effect)
Model evaluation must extend beyond simple metrics to ensure the model is solving the intended task rather than exploiting dataset artifacts. For instance, a medical model might correctly identify malignant lesions by detecting rulers placed in photographs of high-risk cases, or a classifier might distinguish between wolves and huskies by identifying the presence of snow in the background. Rigorous verification is required to confirm that the model’s predictive signals are semantically valid.