What is ROC curve and define it with different scenarios why we require AUC?

What is ROC curve and define it with different scenarios why we require AUC?

AUC – ROC curve is a performance measurement for the classification problems at various threshold settings. ROC is a probability curve and AUC represents the degree or measure of separability. It tells how much the model is capable of distinguishing between classes.

Is ROC good 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.

Why is precision-recall curve better for Imbalanced data?

FPR is considered better when it’s smaller since it indicates fewer false positives. In imbalanced data, the FPR tends to stay at small values due to the large numbers of negatives (i.e. making the denominator large). Thus, FPR becomes less informative for the model performance in this situation.

What is one advantage of using a precision recall curve over an ROC curve?

The key difference is that ROC curves will be the same no matter what the baseline probability is, but PR curves may be more useful in practice for needle-in-haystack type problems or problems where the “positive” class is more interesting than the negative class.

What is the difference between ROC and precision recall?

It is only concerned with the correct prediction of the minority class, class 1. A precision-recall curve is a plot of the precision (y-axis) and the recall (x-axis) for different thresholds, much like the ROC curve.

What is the difference between a ROC curve and a precision curve?

There is a very important difference between what a ROC curve represents vs that of a PRECISION vs RECALL curve. Remember, a ROC curve represents a relation between sensitivity (RECALL) and False Positive Rate (NOT PRECISION). Sensitivity is the other name for recall but the False Positive Rate is not PRECISION.

What is the difference between a precision-recall curve and a randomroc curve?

ROC curves are appropriate when the observations are balanced between each class, whereas precision-recall curves are appropriate for imbalanced datasets. Kick-start your project with my new book Probability for Machine Learning, including step-by-step tutorials and the Python source code files for all examples.

What causes precision recall curve to be skewed?

Typically, a model that produces a precision-recall curve that is closer to the top-right corner is better than a model that produces a precision-recall curve that is skewed towards the bottom of the plot. Class imbalance happens when the number of outputs in one class is different from the number of outputs in another class.