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Most real-world classification problems are imbalanced. Fraud, disease, churn, and defects are rare by nature. Standard classifiers chase accuracy, so they quietly ignore the very class you care about. For years, SMOTE was the reflex fix that everyone reached for first. But SMOTE often fails on the messy, high-dimensional data that production systems actually see. The post Handling Imbalanced Classification: What Works Better Than SMOTE appeared first on Analytics Vidhya.

LLMs are getting stronger every day, and building a RAG pipeline has never been easier. Knowing whether it actually works is not. Most teams ship a RAG system, see decent-looking answers, and call it done, until users hit hallucination, missing context, or irrelevant chunks. That’s where evaluation frameworks come in. RAGAS, TruLens, and DeepEval are The post RAG Evaluation Frameworks Compared: RAGAS vs TruLens vs DeepEval appeared first on Analytics Vidhya.

You don't need a PhD to understand fine-tuning. This article explains how pretrained models learn new skills through fine-tuning.