RAG Evaluation Matrix Day 1: BLEU, METEOR, ROUGE + Classification Metrics Explained

Description

👉 RAG Evaluation Matrix Day 1: BLEU, METEOR, ROUGE + Classification Metrics Explained 🎯 Alternative Titles 👉 RAG Evaluation Metrics Explained | BLEU, METEOR, ROUGE 👉 NLP Evaluation Metrics Complete Guide (RAG + LLM) 👉 Evaluation Matrix for AI Systems | Classification vs Generation 📄 Description In this video, we start RAG Evaluation Matrix Day 1 and understand how to evaluate NLP and RAG systems. We cover both classification and generation metrics used in tasks like sentiment analysis, machine translation, summarization, NER, and QA. We dive deep into confusion matrix, accuracy, precision, recall, F1-score, and also advanced generation metrics like BLEU, METEOR, and ROUGE. Finally, we connect these metrics with RAG evaluation including retriever and generator performance. Reference Notebook Here is the GitHub repo link https://github.com/switch2ai 🧠 NLP Tasks Text Summarization Sentiment Analysis Machine Translation NER Question Answering 📊 Classification Metrics Tasks Sentiment Analysis Text Classification NER Spam Detection 📌 Need Labelled data Actual vs Predicted 📊 Confusion Matrix Predicted Positive Predicted Negative Actual Positive TP FN Actual Negative FP TN 📐 Metrics Accuracy Accuracy = (TP + TN) / (TP + TN + FP + FN) ⚠️ Not reliable for imbalanced data Precision Precision = TP / (TP + FP) 👉 Focus: minimize False Positives Example → Spam detection Recall Recall = TP / (TP + FN) 👉 Focus: minimize False Negatives Example → Cancer detection F1 Score F1 = 2 * Precision * Recall / (Precision + Recall) 👉 Single balanced metric 🧠 Generation Metrics Example Hindi Mat ke upar cat hai English There is cat on mat The cat is on the mat 🔥 BLEU (Bilingual Evaluation Understudy) 👉 Precision-based metric 🔹 Idea How many predicted words are present in reference Precision = m / w_translated 🔹 Problem Repetition issue Predicted → cat cat cat Still high score ❌ 🔹 Solution Modified Precision (Clipping) Only count words based on reference frequency 🔹 N-gram Precision Unigram Bigram Trigram Final = product of all 🔹 Brevity Penalty If predicted is shorter → penalty applied BLEU = BP × N-gram Precision 🔥 METEOR 👉 Uses Precision + Recall 👉 More weight to Recall Formula F1 = 2PR / (P + R) Weighted F1 = 10PR / (R + 9P) 🔹 Chunk Penalty Checks word order Penalty = 0.5 × (C/M)^3 Final METEOR = Weighted F1 × Penalty 🔥 ROUGE 👉 Recall-based metric 👉 Used for Summarization Types ROUGE-1 → Unigram ROUGE-2 → Bigram ROUGE-L → Longest Common Subsequence 🤖 RAG Evaluation Retriever Context Precision Context Recall Generator Faithfulness Answer Relevancy 🔥 Key Takeaways Classification → Confusion Matrix BLEU → Precision-based METEOR → Precision + Recall ROUGE → Recall-based RAG → Retriever + Generator evaluation 🚀 Real World Use Chatbots RAG systems LLM evaluation AI products 🔥 Hashtags #RAG #RAGEvaluation #BLEU #METEOR #ROUGE #AI #MachineLearning #DeepLearning #GenAI #Switch2AI 🔍 SEO Tags rag evaluation metrics bleu meteor rouge explained nlp evaluation metrics classification vs generation metrics precision recall f1 score confusion matrix tutorial llm evaluation metrics rag retriever evaluation genai metrics explained advanced rag tutorial 🔍 SEO Tags (500 char) rag evaluation metrics,bleu meteor rouge explained,nlp evaluation metrics,classification vs generation metrics,precision recall f1 score,confusion matrix tutorial,llm evaluation metrics,rag retriever evaluation,genai metrics explained,advanced rag tutorial,bleu score explained,meteor score explained,rouge score explained,Switch 2 AI