Which evaluation technique for classification tasks displays true positives, false positives, and negatives?

The confusion matrix is a fundamental evaluation technique used in classification tasks that provides a comprehensive view of the model's performance. It displays four key metrics: true positives, false positives, true negatives, and false negatives.

True positives represent the cases where the model correctly predicts the positive class. False positives indicate instances where the model incorrectly predicts the positive class when the actual class is negative. Conversely, true negatives show where the model correctly identifies the negative class, and false negatives are cases where the model misses predicting the positive class.

By presenting these metrics in a matrix format, the confusion matrix allows for a clear and detailed analysis of how well the classification model is performing. It helps identify areas where the model may be over-predicting or under-predicting and provides insights into its strengths and weaknesses. This information is crucial for improving the model and ensuring it meets the desired performance criteria for the specific task at hand.

Other techniques, such as ROC curves and precision-recall graphs, focus on different aspects of model evaluation, like trade-offs between true positive rates and false positive rates, or precision versus recall, but do not provide the detailed breakdown of both positive and negative classifications as successfully as the confusion matrix does.