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Rules vs model bake-off: measuring when learned models actually earn their complexity
A machine learning model achieves 77.5% accuracy. A handful of hand-written rules achieves 76.0%. The model wins the benchmark.
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Evaluating RAG systems: faithfulness, relevance, and hallucination rate
A retrieval-augmented generation (RAG) system can produce an answer that sounds authoritative, responds directly to the user’s question, and includes citations—while being completely wrong.
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Evaluating a chatbot: task success, tone, and safety at scale
A chatbot can sound helpful, receive enthusiastic feedback, and still fail at the job it was built to perform.
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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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Evaluating LLMs beyond vibes: building an eval harness that catches regressions
A language model can produce a beautifully written answer that is completely wrong.
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Data leakage: the silent killer of ML projects
Machine learning projects rarely fail because the algorithm is not advanced enough. More often, they fail because the model learned from information it would never have at prediction time.
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Best practices for evaluating clusters
Evaluating clustering models goes far beyond picking the highest silhouette score. This post explores modern best practices for evaluating clusters in unsupervised learning, combining internal and external validation metrics, visualization techniques, and domain-driven evaluation frameworks that leading data teams use in 2025 to ensure meaningful, actionable segmentation results.
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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.