Tag: Evaluation
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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.