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R: Unleash Machine Learning Techniques by Cory Lesmeister, Brett Lantz, Dipanjan Sarkar, Raghav Bali

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Model selection

What are we to make of all this? We have the confusion matrices from our models to guide us, but we can get a little more sophisticated when it comes to selecting the classification models. An effective tool for a classification model comparison is the Receiver Operating Characteristic (ROC) chart. Very simply, ROC is a technique for visualizing, organizing, and selecting the classifiers based on their performance (Fawcett, 2006). On the ROC chart, the y-axis is the True Positive Rate (TPR) and the x-axis is the False Positive Rate (FPR). The following are the calculations, which are quite simple:

  • TPR = Positives correctly classified / total positives
  • FPR = Negatives incorrectly classified / total negatives

Plotting the ROC results ...

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