equivalent to 1 minus False Positive Rate.True Negative Rate: When it's actually no, how often does it predict no?.False Positive Rate: When it's actually no, how often does it predict yes?.also known as "Sensitivity" or "Recall".True Positive Rate: When it's actually yes, how often does it predict yes?.Misclassification Rate: Overall, how often is it wrong?.Accuracy: Overall, how often is the classifier correct?.This is a list of rates that are often computed from a confusion matrix for a binary classifier: I've added these terms to the confusion matrix, and also added the row and column totals: false negatives (FN): We predicted no, but they actually do have the disease.false positives (FP): We predicted yes, but they don't actually have the disease.true negatives (TN): We predicted no, and they don't have the disease.true positives (TP): These are cases in which we predicted yes (they have the disease), and they do have the disease.
Let's now define the most basic terms, which are whole numbers (not rates): In reality, 105 patients in the sample have the disease, and 60 patients do not.Out of those 165 cases, the classifier predicted "yes" 110 times, and "no" 55 times.The classifier made a total of 165 predictions (e.g., 165 patients were being tested for the presence of that disease).If we were predicting the presence of a disease, for example, "yes" would mean they have the disease, and "no" would mean they don't have the disease. There are two possible predicted classes: "yes" and "no".Let's start with an example confusion matrix for a binary classifier (though it can easily be extended to the case of more than two classes): I wanted to create a "quick reference guide" for confusion matrix terminology because I couldn't find an existing resource that suited my requirements: compact in presentation, using numbers instead of arbitrary variables, and explained both in terms of formulas and sentences. The confusion matrix itself is relatively simple to understand, but the related terminology can be confusing. machine learning Simple guide to confusion matrix terminologyĪ confusion matrix is a table that is often used to describe the performance of a classification model (or "classifier") on a set of test data for which the true values are known.
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