Model evaluation metrics are standards used to measure the performance of a machine learning model.
They help determine whether a model is accurate, reliable, and ready for real-world use.
Different metrics are used for classification and regression tasks.
Classification Metrics
Used when the output is a category or label.
- Accuracy: % of correct predictions
- Precision: Correct positive predictions ÷ total predicted positives
- Recall: Correct positive predictions ÷ total actual positives
- F1-Score: Harmonic mean of precision and recall
- Confusion Matrix: Shows true positives, true negatives, false positives, and false negatives
Regression Metrics
Used when the output is a continuous value.
- Mean Absolute Error (MAE): Average absolute difference between predicted and actual values
- Mean Squared Error (MSE): Average squared difference
- Root Mean Squared Error (RMSE): Square root of MSE
- R² Score: Measures how well predictions match actual values
Why Evaluation Metrics Are Important
- Identify model strengths and weaknesses
- Compare multiple models
- Avoid overfitting or underfitting
- Guide model improvement and optimization
Real-World Examples
- Checking spam detection accuracy for email filters
- Evaluating predicted house prices vs actual prices
- Measuring recommendation system performance
- Assessing customer churn prediction models
Conclusion
Model evaluation metrics are essential for measuring machine learning success. Choosing the right metric ensures accurate and reliable models.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/