UIHC machine learning tutorial
The 'doc' repository contains the 3 sessions Rmarkdown files. If you open one of these files in Rstudio, you have the option to 'Knit' it and it will generate an HTML document containing R commands.
There is also a Shiny application (interactive), where you can upload your data set and apply some standard machine learning approaches.
Important: if you plan to run the Shiny application, you want to install the following R packages:
- shiny: interactive user interface running in R
- glmnet: generalized linear model with Elastic-Net implementation
- DT: render DataTable, better than Shiny default. Allows to color cells
- ggplot2: nice graphical tool
- RColorBrewer: nice color palette
- randomForest: package for learning ... random forest model
- LiblineaR: one of numerous SVM R packages
When all those packages are installed, you can open server.R in Rstudio and click on the 'Run App' button (top right green arrow).
TODO:
- The 'Classification' part is under development.
- Consider different performance measures if binary or multiclass classification is considered
- Include standard SVM kernels