| ROC (Receiver Operating Characteristic) | AUC (Area Under Curve) |
| – ROC Curve represents relationship between Recall and Specificity. – It is a performance measurement for the binary classification. – It is probability curvet plotted with TPR against the FPR. | – AUC Curve represent the degree of measure of separability. – Higher the AUC, the better the model. – It is the measure of the ability of a classifier to distinguish between classes AUC can never go below 0.5. |
The ROC curve and Bar Plots are very effective way to make decisions on your machine learning model based on how important it is to not allow false positives or false negatives. It represents the relationship between Recall and specificity, which helps to assess the best threshold value for the optimal model.
AUC – ROC curve is a performance measurement for the classification problems at various threshold settings. ROC is a probability curve and AUC represent the degree or measure of separability. It tells how much the model is capable of distinguishing between classes. Higher the AUC, the better the model is at predicting 0 classes as 0 and 1 classes as 1. By analogy, the Higher the AUC, the better the model is at distinguishing between patients with the disease and no disease.
The ROC curve is plotted with TPR against the FPR where TPR is on the y-axis and FPR is on the x-axis.
The Receiver Operator Characteristic (ROC) curve is an evaluation metric for binary classification problems. It is a probability curve that plots the TPR against FPR at various threshold values and essentially separates the ‘signal’ from the ‘noise’. The Area Under the Curve (AUC) is the measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curve.
The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes.
ROC curves visualize all possible thresholds.
Misclassification rate is error rate for a single threshold.
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