What is AUC model?
AUC (Area under the ROC Curve). AUC provides an aggregate measure of performance across all possible classification thresholds. One way of interpreting AUC is as the probability that the model ranks a random positive example more highly than a random negative example.
How do you find the AUC of a model?
The AUC can be computed by adjusting the values in the matrix so that cells where the positive case outranks the negative case receive a 1 , cells where the negative case has higher rank receive a 0 , and cells with ties get 0.5 (since applying the sign function to the difference in scores gives values of 1, -1, and 0 …
What is a good AUC for a model?
AREA UNDER THE ROC CURVE In general, an AUC of 0.5 suggests no discrimination (i.e., ability to diagnose patients with and without the disease or condition based on the test), 0.7 to 0.8 is considered acceptable, 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding.
When to pay attention to the validation AUC score?
However, I am confused about which AUC score to pay attention to when it comes to describing the performance of the classifiers as well as deciding which one is the most optimal one – do I pay attention to the validation AUC score or to the test AUC score?
How to do 5 fold cross validation in machine learning?
I’ve split my data into a training and test set (75% and 25% split respectively), and have subsequently performed a 5-fold cross validation on the training set using GridSearchCV in order to find the optimal hyperparameters for four different classification models (LR, RF, SVM and k-NN).
How is the validation data set used in machine learning?
Different models have different parameters that might need to be tuned (such a the ‘k’ in k-NN or the number of trees in RF) and the validation data set is used to choose select the best values for these parameters.