Evaluating Models | CBSE Class 10 AI | Accuracy, Precision, Recall, F1 Score & Confusion Matrix

📘 CBSE Class 10 Artificial Intelligence – Chapter: Evaluating Models

In this video, we will dive deep into the concept of Model Evaluation in AI as per the CBSE Class X AI Curriculum (2025-26). Learn all the important evaluation metrics like Accuracy, Precision, Recall, F1 Score, and Confusion Matrix through clear explanations and examples.

🎯 This video covers:

What is Evaluation in AI?

Importance of Evaluating AI Models

Train-Test Split: Need and Significance

Overfitting Explained with Examples

Accuracy vs. Error – Key Differences

Evaluation Metrics for Classification Problems

Understanding Prediction vs. Reality

Output Cases: TP, TN, FP, FN

What is a Confusion Matrix?

How to Calculate Accuracy, Precision, Recall & F1 Score

When Accuracy is Misleading – Drawbacks

Situations where False Positives/Negatives Matter

Ethical Concerns in Model Evaluation – Bias, Transparency & Accountability

Sample CBSE question for practice

🧠 Perfect for exam preparation, understanding AI concepts, and scoring high in your board exams!

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