Evaluating Models Class 10 AI (Code 417) | Accuracy, Precision, Recall & F1 Score Explained скачать в хорошем качестве

Evaluating Models Class 10 AI (Code 417) | Accuracy, Precision, Recall & F1 Score Explained

Evaluating Models Class 10 AI Code 417 made crystal clear—learn how AI models are tested, trusted, and improved using accuracy, precision, recall & F1 score in one powerful session. This lecture takes you inside Evaluating Models – Unit 3 (Part B, 10 Marks) of CBSE Class 10 Artificial Intelligence (AI 417), where models stop guessing and start proving themselves. If you’ve ever wondered how we decide whether an AI model is good or dangerous, this is where the truth lives. Using board-aligned language, real-life analogies, and step-by-step activities, you’ll learn why accuracy alone can mislead, how confusion matrices expose model behaviour, and when precision, recall, or F1 score becomes the right choice—especially for unbalanced datasets like medical diagnosis or fraud detection. This is not decorative theory. This is exam-ready clarity, grounded in CBSE expectations and extended toward real-world AI thinking—perfect for Class 9–12 students, competitive learners, and anyone stepping into BCA, MCA, BTech, or MTech foundations. ➡️ Notes & PDFs: www.SinghClasses.in ➡️ Join my WhatsApp Group: chat.whatsapp.com/LW4dBrAHIaGAXwK1ni2MPH 🎯 What You’ll Learn ✔️ Why model evaluation is the backbone of trustworthy AI ✔️ Train–Test Split: concept, need & overfitting prevention ✔️ Accuracy vs Error — and why accuracy can lie ✔️ Hands-on accuracy calculation activity (House Price Prediction) ✔️ Confusion Matrix explained from scratch (TP, FP, FN, TN) ✔️ When to use Precision (False Positives matter) ✔️ When to use Recall (False Negatives matter) ✔️ F1 Score as a balance metric for unbalanced datasets ✔️ Ethical concerns in model evaluation: bias, transparency & accountability ✔️ Board-level numericals + real-life AI scenarios 👨‍🏫 About the Instructor – Rohit Singh PGT Computer Science | 10+ years shaping board results, not just lectures Specialist in Class 10–12 CBSE 📌 Artificial Intelligence (417) | Computer Science (085) | Informatics Practices (065) Classes Available: 📍 Offline Coaching: Sant Nagar & Hudson Lane 📞 Academic Counselling & Enrolments: 8800873871 👍 Call to Action If this video helped you see beyond formulas, Like, Share, and Subscribe. Drop your doubts in the comments—I read them, I reply, and I teach through them. 💬 Question for You (Comment Below 👇) 👉 In real-life AI systems like medical diagnosis or fraud detection, which metric matters more—Precision or Recall, and why? Your answer tells me how deeply you understand AI. ⏱️ Timestamps – Study Smart, Not Long 00:00 – Introduction to Evaluating Models (Unit 3 Overview) 01:08 – Why Model Evaluation Matters in Artificial Intelligence Methods of Model Evaluation (Overview) 03:13 – Train–Test Split: Concept & Need 05:46 – Accuracy vs Error 08:01 – Activity 1: Accuracy Calculation (House Price Prediction) 09:45 – Evaluation Metrics for Classification 10:42 – Confusion Matrix in Detail with Activity 22:00 – Accuracy: Concept with Example 24:47 – Precision: Concept with Example 26:50 – Recall: Concept with Example 28:04 – F1 Score: Concept with Example 30:07 – Ethical Concerns in Model Evaluation 30:30 – Outro – Subscribe, Like & Share 🔖 Hashtags #CBSEClass10AI #AI417 #EvaluatingModels #MachineLearningClass10 #ModelEvaluation #PrecisionRecall #F1Score #ConfusionMatrix #BoardExamAI #RohitSingh #SinghClasses