Evaluation

Evaluating Classification Models

How do we know if our classification model is good? We use various metrics derived from the

Confusion Matrix

A confusion matrix is a table that shows the number of correct and incorrect predictions, broken down by each class: True Positives (TP): Correctly predicted positives False Positives (FP): Incorrectly predicted positives (Type I error) False Negatives (FN): Incorrectly predicted negatives (Type II error) True Negatives (TN): Correctly predicted negatives

From these values, we can calculate various metrics:

• Accuracy: $\frac{TP + TN}{TP + TN + FP + FN}$ - The percentage of all predictions that are correct

• Precision: $\frac{TP}{(TP + FP)}$ - The percentage of positive predictions that are correct

• Sensitivity (Recall): $\frac{TP}{(TP + FN)}$ - The percentage of actual positives that were correctly identified

• Specificity: $\frac{TN}{(TN + FP)}$ - The percentage of actual negatives that were correctly identified

Different applications might prioritize different metrics. For example, a cancer detection model might prioritize high recall (sensitivity) to ensure it catches as many cancer cases as possible, even if that means more false positives.

Machine Learning in Action

Machine learning is all around us! Here are some examples:

• Object Recognition: Identifying objects, people, and animals in images
• Digit Recognition: Reading handwritten numbers (used for postal codes and check processing)
• Spam Detection: Filtering out unwanted emails
• Recommendation Systems: Suggesting products, movies, or songs you might like

Final Thoughts

Machine learning is a powerful tool that's transforming our world. By understanding the basics of how these systems learn from data, you're taking your first steps into an exciting field with endless possibilities!

Remember, the key to success in machine learning is finding the right balance: models that are complex enough to capture real patterns but simple enough to generalize well to new data.