Load in DHFR data, type: librarydatasets and then datadhfr
7
Perform summary statistics
8
Use skimr package to explore the data
9
Make a scatter plot
10
Make a histogram
11
Make feature plots
12
Let's build the DHFR classification model
13
Load in the libraries
14
Set the seed for reproducibility
15
Build the training and CV models
16
Let's look at prediction results
17
Let's make Feature importance plots
Description:
Learn how to repurpose machine learning code in R for new datasets in this comprehensive tutorial video. Explore the process of adapting existing R code to model a new dataset, specifically focusing on the DHFR (dihydrofolate reductase) data. Follow along as the instructor guides you through launching RStudio, loading and exploring the DHFR dataset, performing summary statistics, creating visualizations, and building a classification model. Gain practical skills in data understanding, feature analysis, and model building while learning how to effectively reuse and modify R code for different datasets.
Machine Learning in R - Repurpose Machine Learning Code for New Data