The F-score is a metric for evaluating classification models. The common F1 score is the harmonic mean of precision (how many of the predicted hits are correct) and recall (how many of the actual hits were found), and ranges from 0 to 1. It is especially useful with imbalanced data: a spam filter that almost never flags spam can have high accuracy but a low F1 score.
Recall (also called sensitivity or true positive rate) measures what share of the actual positive cases a model detects: correctly identified positives divided by all real positives. If a fraud filter catches 80 out of 100 real fraud cases, its recall is 0.8. High recall matters when missed cases are costly, such as in medical diagnosis. Because it ignores false alarms, it is usually considered together with precision, for example in the F-score.
A confusion matrix is a table that compares a classifier's predictions with the actual classes. For two classes, it shows true positives, false positives, true negatives and false negatives. From this, metrics such as precision, recall and F1 score can be calculated, and it becomes clear which errors occur. Example: a spam filter that flags many genuine emails as spam has many false positives, which is often more costly than a missed spam message.
The area under the curve (AUC) is a metric for classification models, usually referring to the ROC curve. It indicates how well a model separates positive from negative cases, independently of any particular threshold. A value of 1 means perfect separation; 0.5 is no better than guessing. Example: a churn model with an AUC of 0.85 ranks a randomly chosen customer who leaves as riskier than a loyal one in 85% of cases.
Top-k accuracy measures how often the correct answer is among a model's k most likely suggestions. With top-1 only the first prediction counts; with top-5 it is enough for the correct class to be among the five best, a well-known metric from image recognition benchmarks such as ImageNet. It is useful wherever users see several suggestions, for example product recommendations or autocomplete.
