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Mastering Machine Learning with R - Second Edition by Cory Lesmeister

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Model selection

What are we to conclude from all this work? We have the confusion matrices and error rates 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 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 ...

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