{"id":13665435,"url":"https://github.com/dlab-berkeley/Machine-Learning-in-R","last_synced_at":"2025-04-26T08:32:15.189Z","repository":{"id":44784712,"uuid":"81277064","full_name":"dlab-berkeley/Machine-Learning-in-R","owner":"dlab-berkeley","description":"Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles","archived":true,"fork":false,"pushed_at":"2021-03-25T18:40:41.000Z","size":22990,"stargazers_count":187,"open_issues_count":2,"forks_count":72,"subscribers_count":19,"default_branch":"master","last_synced_at":"2024-08-02T06:02:14.844Z","etag":null,"topics":["cluster","decision-trees","dlab-berkeley","lasso","machine-learning","pca","random-forest","superlearner","tutorial","xgboost"],"latest_commit_sha":null,"homepage":"https://dlab-berkeley.github.io/Machine-Learning-in-R/slides.html","language":"CSS","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dlab-berkeley.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-02-08T02:13:06.000Z","updated_at":"2024-07-30T16:52:20.000Z","dependencies_parsed_at":"2022-09-03T05:02:08.364Z","dependency_job_id":null,"html_url":"https://github.com/dlab-berkeley/Machine-Learning-in-R","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dlab-berkeley%2FMachine-Learning-in-R","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dlab-berkeley%2FMachine-Learning-in-R/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dlab-berkeley%2FMachine-Learning-in-R/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dlab-berkeley%2FMachine-Learning-in-R/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dlab-berkeley","download_url":"https://codeload.github.com/dlab-berkeley/Machine-Learning-in-R/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224031926,"owners_count":17244361,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cluster","decision-trees","dlab-berkeley","lasso","machine-learning","pca","random-forest","superlearner","tutorial","xgboost"],"created_at":"2024-08-02T06:00:38.514Z","updated_at":"2024-11-11T00:30:47.112Z","avatar_url":"https://github.com/dlab-berkeley.png","language":"CSS","readme":"# See the Fall 2020 tidymodels update!\nhttps://github.com/dlab-berkeley/Machine-Learning-with-tidymodels\n\n# Machine Learning in R\n\nThis 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).\n\nRStudio Binder:\n[![Binder](http://mybinder.org/badge.svg)](http://beta.mybinder.org/v2/gh/dlab-berkeley/Machine-Learning-in-R/master?urlpath=rstudio)\n\n## Content outline\n\n  - Background on machine learning\n      - Classification vs regression\n      - Performance metrics\n  - Data preprocessing\n      - Missing data\n      - Train/test splits\n  - Algorithm walkthroughs\n      - Lasso\n      - Decision trees\n      - Random forests\n      - Gradient boosted machines\n      - SuperLearner ensembling\n      - Principal component analysis  \n      - Hierarchical agglomerative clustering  \n  - Challenge questions  \n  \n## Getting started\n\nPlease follow the notes in [participant-instructions.md](participant-instructions.md).  \n\n#### HAVE FUN! :^)\n\nThe seven algorithm R Markdown files (lasso, decision tree, random forest, xgboost, SuperLearner, PCA, and clustering) are designed to function in a standalone manner.  \n\nAfter 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! \n\n## Assumed participant background\n\nWe assume that participants have familiarity with:\n\n* Basic R syntax\n* Statistical concepts such as mean and standard deviation\n\n## Technology requirements\n\nPlease bring a laptop with the following:\n\n* [R version](https://cloud.r-project.org/)\n3.5 or greater\n* [RStudio integrated development environment (IDE)](https://www.rstudio.com/products/rstudio/download/#download) is\nhighly recommended but not required.\n\n## Resources\n\nBrowse 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!  \n\n## Slideshow\n\nThe 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).  \n\n","funding_links":[],"categories":["CSS"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdlab-berkeley%2FMachine-Learning-in-R","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdlab-berkeley%2FMachine-Learning-in-R","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdlab-berkeley%2FMachine-Learning-in-R/lists"}