Understanding model interpretability in R with ggplot2 and mikropml (CC134)

August 5, 2021 • PD Schloss • 1 min read • •

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 ggplot2 and dplyr packages in RStudio.

Code

You can browse the state of the repository at the

Installations

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 homebrew and git

  /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 “mikropml_demo”
  - 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 git@github.com:SchlossLab/mikropml_demo.git
cd mikropml_demo
git reset --hard f5d8afe98b6d0586cf51aff6c7792e270e58fad4
git remote set-url origin git@github.com:<your_github_id>/mikropml_demo.git
git push -u origin master
```
- Return to GitHub and refresh your browser.
- Go to the mikropml_demo directory on your computer and double click on the mikropml_demo.Rproj icon. This will launch RStudio and you’ll be good to go.