Chapter 04.09: ROC Basics
From the confusion matrix we can calculate a variety of ROC metrics. Among others, we will explain true positive rate, negative predictive value and the $F1$ measure.
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## Which statements are true?
- [x] Logistic regression minimizes the binomial loss.
- [x] The Brier score is like the MSE, just with probabilities.
- [ ] The log-loss punishes being very wrong less than the Brier score.
- [ ] Accuracy and mean classification error are calculated using the predicted probabilities.
- [x] The confusion matrix tabulates the true against predicted classes.
- [ ] A misclassification error rate of 0.1% is always great.