![caret confusion matrix caret confusion matrix](https://askcodez.com/images3/15767545306352.png)
When using twoClassSummary(), be sure to always include the argument classProbs = TRUE or your model will throw an error! (You cannot calculate AUC with just class predictions. The twoClassSummary() convenience function allows you to do this easily. You can use the trainControl() function in caret to use AUC (instead of accuracy), to tune the parameters of your models. This is often a much more useful metric than simply ranking models by their accuracy at a set threshold, as different models might require different calibration steps (looking at a confusion matrix at each step) to find the optimal classification threshold for that model. An AUC of 0.5 is no better than random guessing, an AUC of 1.0 is a perfectly predictive model, and an AUC of 0.0 is perfectly anti-predictive (which rarely happens).
![caret confusion matrix caret confusion matrix](https://user-images.githubusercontent.com/43855029/120687356-efe35880-c46f-11eb-950f-5feef237a4c1.png)
Specificity: true negative rate (TNR), selectivity
![caret confusion matrix caret confusion matrix](https://www.statology.org/wp-content/uploads/2021/04/confusionR1.png)
Sensitivity: true positive rate (TPR), recall, hit rate Negative predictive value (NPV) = TN / (TN + FN)įalse negative rate (FNR) = 1 - Sensitivityįalse positive rate (FPR) = 1 - Specificity Positive predictive value (PPV) = TP / (TP + FP) The test checks if there is a significant difference between the counts in these two cells. Specifically, the No/Yes and Yes/No (A/B and B/A in your case) cells in the confusion matrix. McNemar’s Test captures the errors made by both models. The statistics returned in the stats element are:Īccuracy = (TP + TN) / (TP + TN + FP + FN) Interpret the McNemar’s Test for Classifiers. Matrix (contingency table) to be of the form: Stats a matrix of summary statistics and confidence intervals.įor the sensitivity and specificity function we expect the 2-by-2 confusion The sensitivity and specificity functions return numeric values.Ĭonfusion_matrix returns a list with elements: Environment containing the variables listed in the formula Value