When to use ROC AUC in cross validation?
I use the code to run cross validation, returning ROC scores. ROC AUC is only useful if you can rank order your predictions. Using .predict () will just give the most probable class for each sample, and so you won’t be able to do that rank ordering.
How are AUC and logloss related in binary classification?
As you mention, AUC is a rank statistic (i.e. scale invariant) & log loss is a calibration statistic. One may trivially construct a model which has the same AUC but fails to minimize log loss w.r.t. some other model by scaling the predicted values. Consider: So, we cannot say that a model maximizing AUC means minimized log loss.
How is it possible that validation loss is possible?
For example, if an image of a cat is passed into two models. Model A predicts {cat: 0.9, dog: 0.1} and model B predicts {cat: 0.6, dog: 0.4}. Both model will score the same accuracy, but model A will have a lower loss. Because of this the model will try to be more and more confident to minimize loss.
How is it possible to cross entropy loss?
A model can overfit to cross entropy loss without over overfitting to accuracy. For example, if an image of a cat is passed into two models. Model A predicts {cat: 0.9, dog: 0.1} and model B predicts {cat: 0.6, dog: 0.4}. Both model will score the same accuracy, but model A will have a lower loss.
How to calculate ROC curves using validation data?
Include a SCORE statement to apply the fitted model to the validation data set (VALID) and create a data set of predicted event probabilities (VALPRED). The OUTROC= options in the MODEL and SCORE statements produce plots of the ROC curves for the training and validation data sets (when ODS graphics is on) and also saves the ROC data in a data set.
How to use Receiver Operating Characteristic with cross validation?
Receiver Operating Characteristic (ROC) with cross validation ¶ Example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality using cross-validation. ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis.
Which is better, the ROC curve or the AUC?
This is not very realistic, but it does mean that a larger area under the curve (AUC) is usually better. The “steepness” of ROC curves is also important, since it is ideal to maximize the true positive rate while minimizing the false positive rate.