Classification

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Essential Reading

Understanding Classifications

• 4 Types of Classification Tasks in Machine Learning

• [Regression and Classification

  Supervised Machine Learning](https://www.geeksforgeeks.org/regression-classification-supervised-machine-learning/)

• Ch 4 - Classification of book Introduction to Statistical Learning offers good overview of Classification.

Classification Metrics

• The 5 Classification Evaluation metrics every Data Scientist must know
• Performance Metrics for Classification problems in Machine Learning

Confusion Matrix

• Quick intro to confusion matrix - video
• Simple guide to confusion matrix terminology
• What is a Confusion Matrix in Machine Learning

ROC Curve

• Understanding AUC - ROC Curve
• ROC and AUC, Clearly Explained! - video

Classification Metrics in Sci-kit Learn

• sklearn model evaluation

Extra Reading

• Tour of Evaluation Metrics for Imbalanced Classification

Checklist

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?

Exercises

Difficulty Level

★☆☆ - Easy
★★☆ - Medium
★★★ - Challenging
★★★★ - Bonus

EX-1: Draw a Confusion Matrix (★☆☆)

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)	???	???

EX-2: Draw a Confusion Matrix (★★☆)

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

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