- 4 Types of Classification Tasks in Machine Learning
- Regression and Classification | Supervised Machine Learning
- Ch 4 - Classification of book Introduction to Statistical Learning offers good overview of Classification.
- The 5 Classification Evaluation metrics every Data Scientist must know
- Performance Metrics for Classification problems in Machine Learning
- Quick intro to confusion matrix - video
- Simple guide to confusion matrix terminology
- What is a Confusion Matrix in Machine Learning
Check your knowledge:
- How is classification different from regression?
- What is difference between binary classification and multi-class classification? Give some examples
- What are some of the different classification algorithms?
- How do we test the accuracy of an algorithm?
- Understand the following terms:
- Confusion matrix
- accuracy
- true positive
- false negative
- What is ROC curve? How is that useful?
- What is AUC? How is that used?
★☆☆ - Easy
★★☆ - Medium
★★★ - Challenging
★★★★ - Bonus
We have 10 type-A, and 20 type-B.
Imagine, we have a perfect classifier.
Draw a perfect confusion matrix
| Predicted A | Predicted B | |
|---|---|---|
| Actual A (10) | ??? | ??? |
| Actual B (20) | ??? | ??? |
From the previous example, the classifier produced the following:
- for class A, out of 10 test samples, it predicted 7 correctly as A. 3 it mis-predicted as B
- for class B, out of 20 test samples, it predicted 15 correctly as B, 5 it mis-predicted as A
Draw the confusion matrix
| Predicted A | Predicted B | |
|---|---|---|
| Actual A (10) | ??? | ??? |
| Actual B (20) | ??? | ??? |
And calculate the following:
- accuracy
- precision
- recall