See the Fall 2020 tidymodels update!
https://github.com/dlab-berkeley/Machine-Learning-with-tidymodels
Machine Learning in R
This is the repository for D-Lab’s Introduction to Machine Learning in R workshop. View the associated slides here.
RStudio Binder:
Content outline
- Background on machine learning
- Classification vs regression
- Performance metrics
- Data preprocessing
- Missing data
- Train/test splits
- Algorithm walkthroughs
- Lasso
- Decision trees
- Random forests
- Gradient boosted machines
- SuperLearner ensembling
- Principal component analysis
- Hierarchical agglomerative clustering
- Challenge questions
Getting started
Please follow the notes in participant-instructions.md.
HAVE FUN! :^)
The seven algorithm R Markdown files (lasso, decision tree, random forest, xgboost, SuperLearner, PCA, and clustering) are designed to function in a standalone manner.
After installing and librarying the packages in 01-overview.Rmd, run all the code in 02-preprocessing.Rmd to preprocess the data. Then, open any one of the seven algorithm R Markdown files and "Run All" code to see the results and visualizations!
Assumed participant background
We assume that participants have familiarity with:
- Basic R syntax
- Statistical concepts such as mean and standard deviation
Technology requirements
Please bring a laptop with the following:
- R version 3.5 or greater
- RStudio integrated development environment (IDE) is highly recommended but not required.
Resources
Browse resources listed on the D-Lab Machine Learning Working Group repository. Scroll down to see code examples in R and Python, books, courses at UC Berkeley, online classes, and other resources and groups to help you along your machine learning journey!
Slideshow
The slides were made using xaringan, which is a wrapper for remark.js. Check out Chapter 7 if you are interested in making your own! The theme borrows from Brad Boehmke's presentation on Decision Trees, Bagging, and Random Forests - with an example implementation in R.