R code to accompany Real-World Machine Learning (Chapter 4)

Abstract

In the latest update to the rwml-R Github repo, I provide R code to accompany Chapter 4 of the book “Real-World Machine Learning” by Henrik Brink, Joseph W. Richards, and Mark Fetherolf. Topics covered include optimization of model parameters via grid search with caret, plotting a confusion matrix with ggplot2, and generating ROC curves with ROCR. This blog post provides a summary and some examples of the code contained in the update.

rwml-R project pages posted

For convenience, I’ve created a project page for rwml-R to post the generated HTML files from knitr. This (and Chapter 2 and Chapter 3) blog posts are short summaries of the R code provided in the rwml-R project. Also, feel free to fork the rwml-R repo and submit a pull request if you wish to contribute.

Plotting a confusion matrix

The MNIST dataset of handwritten digits makes another appearance. The kknn package is again used, and the confusion matrix is plotted using ggplot2. The color scale for the plot is generated using the RColorBrewer package.

Plotting a series of ROC curves

The ROCR package is introduced and used to generate ROC curves. Also, AUC values are calculated for each curve and displayed along with each of the curves.

Tuning model parameters

The caret package is used to tune parameters via grid search for the Support Vector Machines model with a Radial Basis Function Kernel. By setting summaryFunction = twoClassSummary in trainControl, the ROC curve is used to select the optimal model. The doMC package is also introduced for parallel computation.

Feedback welcome

If you have any feedback on the rwml-R project, please leave a comment below or use the Tweet button. Again, feel free to fork the rwml-R repo and submit a pull request if you wish to contribute.

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