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.
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.
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.
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.
Log loss (logarithmic loss) measures how well the predicted probabilities of a classification model match the actual outcomes. Confident but wrong predictions are penalised particularly heavily; a value close to zero indicates well-calibrated forecasts. Example: if a model gives a 95% churn probability for a customer who stays, the log loss rises sharply. Mathematically it is the same as binary cross-entropy.
