Tutorial for Advanced Data After Dark in Machine Learning
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MLtutorial.Rproj
hintSheet.Rmd
irisLdaCTrees.Rmd
ldaAssignment.Rmd
readme.md
treeAssignment.Rmd

readme.md

#Short Tutorial about Machine Learning and Reproducible Scripting

This is a short tutorial about machine learning and reproducible scripting. For the tutorial, we use a subset of the Wisconsin Breast Cancer Dataset (https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)).

In order to run this, you will need to have Rstudio, a recent version of R (3.0 or greater) and the following packages installed.

  • knitr
  • MASS
  • tree

You will open the RMarkdown (.Rmd) files, which consist of the following files:

  • irisLdaCTrees.Rmd - Example Rmarkdown showing how to run Linear Discriminant Analysis (lda) and Classification trees on the iris dataset.
  • ldaAssignment.Rmd - Worksheet to fill out for LDA analysis on Breast Cancer Dataset. Adapt code from irisLdaCTrees.Rmd and place in here with your interpretation of the results. When you're done, you can knit the results using the knit command above the window.
  • treeAssignment.Rmd - Worksheet to fill out for Classification Tree analysis on Breast Cancer Dataset.

Data Files consist of the following

  • data/wdbc-readme.txt - Readme file for the Wisconsin Dataset.
  • data/trainData.txt - Tab Delimited Data File for training your classifiers.
  • data/testData.txt - Tab Delimited Data File for testing your classifiers.

For more info about Rmarkdown, check out my tutorial: http://github.com/laderast/magic-of-markdown