Understanding model interpretability in R with ggplot2 and mikropml (CC134)
The interpretability of a machine learning model tends to vary by the performance of the model. The need to interpret your model depends on what you hope to do with that model. In this Code Club, Pat shows how you can extract interpretability data from models created using mikropml and visualize the importance of features that are used in the model.
Pat will use functions from the
mikropml R package and the
dplyr packages in
You can browse the state of the repository at the
If you haven’t been following along, you can get caught up by doing the following:
- (windows) Install the Ubuntu Linux BASH shell for Windows 10
- (mac) Install
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" brew install git
- To get to where we are at the beginning of this episode (you won’t have the same issue numbers at Pat)…
- Set up a GitHub account
- Create a new GitHub repository
- Call it “Schloss_rrnAnalysis_XXXX_2020” (feel free to use your own last name)
- Make it Public
- Don’t check the box next to “Initialize this repository with a README”
- Click the green “Create repository” button
Go to your command line and enter the following replacing
<your_github_id>with your GitHub user id
git clone firstname.lastname@example.org:SchlossLab/mikropml_demo.git cd mikropml_demo git reset --hard f5d8afe98b6d0586cf51aff6c7792e270e58fad4 git remote set-url origin email@example.com:<your_github_id>/mikropml_demo.git git push -u origin master
- Return to GitHub and refresh your browser.
- Go to the
mikropml_demodirectory on your computer and double click on the
mikropml_demo.Rprojicon. This will launch RStudio and you’ll be good to go.