https://github.com/dlab-berkeley/Machine-Learning-in-R

Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles
https://github.com/dlab-berkeley/Machine-Learning-in-R

cluster decision-trees dlab-berkeley lasso machine-learning pca random-forest superlearner tutorial xgboost

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Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles

• Host: GitHub
• URL: https://github.com/dlab-berkeley/Machine-Learning-in-R
• Owner: dlab-berkeley
• License: other
• Archived: true
• Created: 2017-02-08T02:13:06.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2021-03-25T18:40:41.000Z (over 3 years ago)
• Last Synced: 2024-01-20T14:14:52.243Z (6 months ago)
• Topics: cluster, decision-trees, dlab-berkeley, lasso, machine-learning, pca, random-forest, superlearner, tutorial, xgboost
• Language: CSS
• Homepage: https://dlab-berkeley.github.io/Machine-Learning-in-R/slides.html
• Size: 21.9 MB
• Stars: 184
• Watchers: 19
• Forks: 72
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 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](https://dlab-berkeley.github.io/Machine-Learning-in-R/slides.html#1).

RStudio Binder:

[![Binder](http://mybinder.org/badge.svg)](http://beta.mybinder.org/v2/gh/dlab-berkeley/Machine-Learning-in-R/master?urlpath=rstudio)

## 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](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](https://cloud.r-project.org/)

3.5 or greater

* [RStudio integrated development environment (IDE)](https://www.rstudio.com/products/rstudio/download/#download) is

highly recommended but not required.

## Resources

Browse resources listed on the [D-Lab Machine Learning Working Group repository](https://github.com/dlab-berkeley/MachineLearningWG). 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](https://github.com/yihui/xaringan), which is a wrapper for [remark.js](https://remarkjs.com/#1). 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](https://bradleyboehmke.github.io/random-forest-training/slides-source.html#1).