Classification
Overview
1. Introduction
2. Application
3. EDA
4. Learning Process
5. Bias-Variance Tradeoff
6. Regression (review)
7. Classification
8. Validation
9. Regularisation
10. Clustering
11. Evaluation
12. Deployment
13. Ethics
Lecture outline
- Classification - Logistic regression review
- Classification evaluation metrics
- The expected value framework
Classification problems
Response is categorical, e.g. credit card default (Yes/No), favourite movie types
(Action/Drama/Animation)
Exemplary techniques - logistic regression, classification tree, K-NN, etc.
Logistic regression formulation
Logistic regression coefficients are estimated by
maximising the likelihood function