When would you use a ROC curve?

When would you use a ROC curve?

ROC curves are frequently used to show in a graphical way the connection/trade-off between clinical sensitivity and specificity for every possible cut-off for a test or a combination of tests. In addition the area under the ROC curve gives an idea about the benefit of using the test(s) in question.

Why ROC curve is not good for Imbalanced Data?

Although widely used, the ROC AUC is not without problems. For imbalanced classification with a severe skew and few examples of the minority class, the ROC AUC can be misleading. This is because a small number of correct or incorrect predictions can result in a large change in the ROC Curve or ROC AUC score.

What is the shape of the ROC for a perfect classifier?

For a perfect classifier the ROC curve will go straight up the Y axis and then along the X axis. A classifier with no power will sit on the diagonal, whilst most classifiers fall somewhere in between.

Is ROC curve for Imbalanced Data?

ROC curves are appropriate when the observations are balanced between each class, whereas precision-recall curves are appropriate for imbalanced datasets. In both cases the area under the curve (AUC) can be used as a summary of the model performance.

Is ROC affected by class imbalance?

The ROC AUC is sensitive to class imbalance in the sense that when there is a minority class, you typically define this as the positive class and it will have a strong impact on the AUC value. This is very much desirable behaviour.

What is classifier in Roc?

The Receiver Operator Characteristic (ROC) curve is an evaluation metric for binary classification problems. When AUC=0.5, then the classifier is not able to distinguish between Positive and Negative class points. Meaning either the classifier is predicting random class or constant class for all the data points.

What is the ROC AUC value of an ideal classifier?

What is the value of the area under the roc curve (AUC) to conclude that a classifier is excellent? The AUC value lies between 0.5 to 1 where 0.5 denotes a bad classifer and 1 denotes an excellent classifier.

What is AUC – ROC curve in machine learning?

In Machine Learning, performance measurement is an essential task. So when it comes to a classification problem, we can count on an AUC – ROC Curve. When we need to check or visualize the performance of the multi-class classification problem, we use the AUC (Area Under The Curve) ROC (Receiver Operating Characteristics) curve.

What does the ROC curve tell you?

The ROC curve plots out the sensitivity and specificity for every possible decision rule cutoff between 0 and 1 for a model. This plot tells you a few different things. A model that predicts at chance will have an ROC curve that looks like the diagonal green line. That is not a discriminating model.

What is the ROC curve of a random classifier?

Interpreting the ROC curve The ROC curve shows the trade-off between sensitivity (or TPR) and specificity (1 – FPR). Classifiers that give curves closer to the top-left corner indicate a better performance. As a baseline, a random classifier is expected to give points lying along the diagonal (FPR = TPR).

What is the ROC curve in binary classification?

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’.