Classification Methods- Episode 1: Logistic Regression

Course details

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

Abstract

Logistic regression is the most popular approach for modeling response variables that are binary.
It allows us to model the association between the probability of an event occurring and independent variables.
These class of models can also be used for predicting the category to which a new observation
belongs given the independent variables. This seminar describes the use of logistic regression models
for classification (assigning each observation to a category, or class.). Methods for evaluating the accuracy of predictions will be discussed.

All computations will be done in RStudio.

Prerequisites

Background readings

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

Bradley Boehmke & Brandon Greenwell. Hands-On Machine Learning with R. Chapman & Hall/CRC The R Series 2020-02-01.

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer Series in Statistics Springer New York Inc., New York, NY, USA, (2001)

Fee

Free

Venue

online

Instructor

Emmanuel Abatih