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Understanding summary metrics

Understanding summary metrics

From the course: Complete Your First Project in R

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Understanding summary metrics

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- [Narrator] Now that you know how to create decision trees, random force, and SVMs, it is time for you to learn how to evaluate them. You'll be creating confusion matrices and gathering summary metrics for each of the three algorithms. Let's take a moment to understand these concepts. A confusion matrix is a tool used in machine learning to evaluate the accuracy of a model. It displays the predictive values against the actual values to see how many match for each option in a grid-like manner. The diagonal values are correct predictions, while the other values are incorrect predictions. There are multiple summary metrics you will explore. From the confusion matrix, you'll gather true and false positive and true and false negative values that you can use to create some of these summary metrics. True positive will be denoted as TP, false positive as FP, true negative as TN, and false negative as FN. The first summary metric is sensitivity, which is the ratio of how many values were…

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