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

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

  • 28/04/2021
    1:00 pm - 2:30 pm

Course details

statistics 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 examples in the R statistical software package.


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


Machine Learning with R. By Brett Lantz


Fee

Free


Venue

Online


Instructor

Emmanuel Abatih


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