Classification Methods- Episode 2: Linear Discriminant Analysis

Course details

data science seminar | level: intermediate | register now
for questions related to this event, contact ugent@flames-statistics.com
affiliation: Ghent University

Abstract

Even though the logistic regression model is a very powerful classification tool, there are instances where the parameter estimates of this model become unstable. For example: when the classes are well-separated, and in instances where the sample size is small. Amongst the alternatives to these problems, Linear Discriminant Analysis (LDA) is popular especially when we have more than two response classes. Discriminant analysis allows for predicting group membership based on a set of continuous independent variables. It assumes that group membership is mutually exclusive and that the predictor variables are independent. Some extensions of the LDA will be discussed and some results will be demonstrated using MASS package R.

Prerequisites

Background readings

Alboukadel Kassambara. Machine Learning Essentials : Practical Guide in R. STHDA, 2018

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. An Introduction to Statistical Learning : with Applications in R. New York :Springer, 2013

Fee

FREE

Venue

online

Instructor

Emmanuel Abatih