AI Exam Must-Know: Regression Evaluation Metrics Explained

Описание: Regression models require different evaluation metrics than classification models, and understanding these is critical for AI and machine learning exams.

In this video, you’ll learn the most important regression metrics explained clearly and intuitively:

✅ Mean Absolute Error (MAE)
✅ Mean Squared Error (MSE)
✅ Root Mean Squared Error (RMSE)
✅ R-squared (R²) explained simply
✅ When to use each regression metric
✅ Runtime & business metrics (response time, ROI, CSAT)
✅ Common exam traps and distractors

We also cover cross-model metrics that apply to both regression and classification, including:

Average response time

Training sessions vs epochs

Customer satisfaction score (CSAT)

Return on investment (ROI)

Cost per user

📌 Exam Tip:
If a question asks “How close predictions are to real values?” → think MAE / RMSE
If it asks “How much variance is explained?” → think R²

This lesson is ideal for:

AWS Certified AI Practitioner

Machine learning beginners

Data science fundamentals

Exam prep & interviews

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