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Course Details

Course Outline
Learning Outcomes
Materials
Prior Knowledge

Course Outline

Qualitative modelling: An introduction to Linear Discriminant Analaysis (LDA).
Fitting LDA models & model prediction: Using {discrim} to add aditional bindings to {parnsip}, to fit LDA models
Advanced model assessment: V-fold cross validation using {workflows} and {tune}
Penalised regression techniques: Ridge regrssion, lasso and elastic net
Tree-Based Models: Bagging, random forrests and boosting

Learning Outcomes

By the end of the course participants will be able to…

use {parsnip} to build LDA models and visualise boundaries
assess model validity using V-fold cross validation
apply penalised regression techniques that penalises models with a high number of predictor variables
use tree-based models in regression & classification problems, to split the predictor space into numerous simple regions

Materials

Prior Knowledge

It will be assumed that participants are familiar with R. For example inputting data, basic visualisation, basic data structures and use of functions. In addition, attendees should be familar with the concepts covered in our Machine Learning with Tidymodels course.

Training Course Details

Advanced Machine Learning with Tidymodels

Course Level: Advanced