Classification metrics

Beyond Confusion Matrix there are several essential metrics:

• Accuracy: The fraction of total correct predictions.

• Sensitivity (Recall / True Positive Rate): The proportion of actual positive instances that were correctly detected.

• Selectivity (Specificity / True Negative Rate):* The proportion of actual negative instances correctly identified as negative.

• Precision (Positive Predictive Value): The ratio of correct positive predictions to the total number of positive predictions.

• Area Under the ROC Curve (AUC): This is a single-number summary of the ROC curve. It measures the probability that a randomly chosen positive instance will be ranked higher by the model than a randomly chosen negative instance. An AUC of 1.0 represents a perfect classifier, while 0.5 represents random guessing.

• Log Loss (Cross-Entropy): A metric used for models that output probabilities. It penalizes false predictions more heavily when the model is confident in its mistake.

• Brier Score: Used to evaluate the accuracy of probabilistic forecasts. It is calculated as the mean squared error of the predicted probabilities compared to the true one-hot labels.

• Error Rate: The simple complement to accuracy (1−Accuracy), representing the proportion of incorrect predictions.

• Average Precision (AP) and Mean Average Precision (mAP): AP summarizes the Precision-Recall Curve by calculating the area under the interpolated curve. mAP is the average of AP values across multiple classes or queries, frequently used in object detection and information retrieval.