# Metrics#

The following performance metric functions are supported in USEncrypt.ai:

 accuracy_score Computes the accuracy score between labels and predictions. binary_cross_entropy Computes the binary cross-entropy between labels and predictions. categorical_cross_entropy Computes the cross-entropy between labels and predictions. confusion_matrix Generates a confusion matrix based on ground-truth and prediction labels. f1_score Computes the F1 score. mean_squared_error Computes the mean squared error between labels and predictions. precision_score Computes the precision score, calculated as the ratio $$\frac{\text{TP}}{(\text{TP} + \text{FP})}$$, where $$\text{TP}$$ is the number of true positives and $$\text{FP}$$ is the number of false positives. recall_score Computes the recall score, calculated as the ratio $$\frac{\text{TP}}{(\text{TP} + \text{FN})}$$ where $$\text{TP}$$ is the number of true positives and $$\text{FN}$$ is the number of false negatives. save_metrics Saves the performance metrics used in classification to a JSON file.