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 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 classifier is a model or method that assigns inputs to one of several predefined categories. Classic variants such as decision trees or naive Bayes are trained on labelled examples; language models can also sort texts based on a description of the categories. Typical uses include spam detection, sorting customer enquiries by topic and categorising receipts. Quality is measured with metrics such as accuracy, precision and recall.
The ROC curve (receiver operating characteristic) shows how well a classification model separates two classes. For every possible decision threshold it plots the true positive rate (recall) against the false positive rate. The closer the curve reaches towards the top-left corner, the better; the diagonal corresponds to pure guessing. The area under it (AUC) summarises the curve in a single number and helps, for example, when comparing different fraud detection models.
