Learn how to repurpose machine learning code in R for new data by adapting and applying existing R code to model a new dataset. The course covers loading data, performing summary statistics, exploring data using packages, creating visualizations, building classification models, and analyzing prediction results. The teaching method involves practical demonstrations using RStudio or RStudio.cloud. This course is intended for individuals familiar with R programming and interested in applying machine learning techniques to new datasets.
Machine Learning in R - Repurpose Machine Learning Code for New Data
Overview
Syllabus
Launch RStudio or RStudio.cloud
Open iris-data-understanding.R file
Create a copy of iris-data-understanding.R
Save as dhfr-data-understanding.R
What is DHFR?
Load in DHFR data, type: librarydatasets and then datadhfr
Perform summary statistics
Use skimr package to explore the data
Make a scatter plot
Make a histogram
Make feature plots
Let's build the DHFR classification model
Load in the libraries
Set the seed for reproducibility
Build the training and CV models
Let's look at prediction results
Let's make Feature importance plots
Taught by
Data Professor
