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Metrics that match the problem: ROC-AUC vs F1 vs MAE vs RMSE, and when accuracy lies
A machine learning model achieves 99% accuracy . Its dashboard looks impressive. Stakeholders are satisfied, and the team is preparing to deploy it.
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Imbalanced classes: why 99% accuracy can mean your model is useless
Imagine building a fraud detection system that achieves 99% accuracy . Your dashboard looks excellent. Your stakeholders are impressed. The model passes testing and gets deployed.
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Evaluating generative vision: FID, CLIPScore, and human preference
Imagine two generative image models responding to the same prompt:
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Intro to model evaluation metrics
Understanding model evaluation metrics is essential for every machine learning practitioner. This post introduces key concepts such as accuracy, precision, recall, F1-score, and more—explaining when and why to use each. It also highlights modern metrics for generative and fair AI systems, and shows practical examples using popular libraries like scikit-learn and PyTorch Lightning.
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Intro to model evaluation metrics
Learn the fundamentals of model evaluation metrics in machine learning, including accuracy, precision, recall, F1-score, and beyond. This beginner-friendly guide covers classification, regression, and generative model metrics, along with modern fairness tools and practical examples using scikit-learn and PyTorch.