For classification problems, several evaluation metrics are used to assess the performance of a model. The most common include accuracy, precision, recall, F1-score, and the confusion matrix.
Accuracy measures the proportion of correct predictions out of the total number of predictions. Precision calculates the ratio of true positive predictions to the total predicted positives, while recall measures the ratio of true positive predictions to the actual positives. The F1-score is the harmonic mean of precision and recall, offering a balance between the two.
The confusion matrix provides a detailed breakdown of true positives, true negatives, false positives, and false negatives, allowing for deeper insights into the model’s performance. These metrics are used depending on the problem, such as in imbalanced datasets where accuracy might not be sufficient.