Learning Outcome 3 Topic 1 Model Evaluation Metrics

Описание: Model Evaluation Metrics Explained — Classification & Regression (Math + Python Examples)

Learn EVERYTHING about model evaluation metrics for both classification and regression in one clear, simplified, and deeply practical video. Whether you are a beginner in Machine Learning or preparing for interviews, assignments, or real-world projects, this lesson covers all the essential theory and implementation.

*What You Will Learn*

Classification Metrics

Confusion Matrix (TP, TN, FP, FN)
Accuracy
Precision
Recall
F1 Score
Specificity
ROC Curve & AUC
Multiclass evaluation

Regression Metrics

MAE (Mean Absolute Error)
MSE (Mean Squared Error)
RMSE (Root Mean Squared Error)
MAPE (Mean Absolute Percentage Error)
R² Score (Coefficient of Determination)
Adjusted R²
Explained Variance
Median Absolute Error

Mathematics + Python Implementation

Get both:

✔ Mathematical formulas
✔ Intuitive explanation
✔ Real Python code examples
✔ Practical demonstrations on real datasets

Perfect for students, data analysts, AI researchers, and ML practitioners.

Who Should Watch This Video?

This video is built for:

Machine Learning beginners
Data Science students
University/college instructors
Kaggle competitors
Anyone building ML models and wants to evaluate performance correctly


Why Evaluation Metrics Matter

Building a model is only half of Machine Learning.
*Understanding how good your model is*— that’s where evaluation metrics come in.
This video teaches you how to interpret results with confidence.