Classification Methods- Episode 4: Decision Tree Models

Classification Methods- Episode 4: Decision Tree Models

  • 23/06/2021
    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 all previous classification methods considered so far, interest typically was on using some observed characteristics of a case to predict an outcome (binary) . For example, when one applies for a loan from a financial institution, a decision has to be made as to whether the loan will be granted or not based on information provided by the applicant. One approach to this is to ask a series of questions to the customer such as: number of years with the current employer and depending on the outcome, this question can be followed–up with other conditional questions. The series of questions and their responses can then be organized into a decision or classification tree. In this seminar, you will learn how to build a decision tree using a set of decision rules (algorithms). These rules will then be used to predict the outcome of a new individual. We are going to use the rpart and caret packages in R on the bank loan dataset to build and prune our decision tree.


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

gratis


Venue

Online


Instructor

Emmanuel Abatih


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