Classification Methods- Episode 3: K-Nearest Neighbor (the lazy learner)

Course details

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

Abstract

In k-NN classification, one uses the other available observations that are most similar (based on the predictors at hand) to the observation we are trying to predict (classify into a group). In this seminar you are going to be introduced to the KNN algorithm and how to choose K. You will also be introduced to model evaluation and other considerations. The KNN method will be explored using the bank loan data-set in the R statistical software package. The performance of KNN will be compared with those of Logistic regression, Linear Discriminant Analysis and Quadratic discriminant Analysis.

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


-Brett Lantz. Machine Learning with R.

Fee

FREE

Venue

Ghent University-ONLINE

Instructor

dr Emmanuel Abatih