What does AUC for test data less than training data mean?

What does AUC for test data less than training data mean?

I split the training data into a train (75%) and test data (25%). I got an AUC for test data less than training data. does this mean overfitting ? It’s hard to detect overfitting with just one data point.

What are the 6 points of the AUC ROC curve?

Here we have 6 points where P1, P2, P5 belong to class 1 and P3, P4, P6 belong to class 0 and we’re corresponding predicted probabilities in the Probability column, as we said if we take two points belonging to separate classes then what is the probability that model rank orders them correctly

How to detect overfitting with just one data point?

It’s hard to detect overfitting with just one data point. Overfitting happens when the performance on the training set is improved but the performance on the test data gets worse. What you got is consistent with overfitting but it could also be due to some other random difference between the training and test data.

What does AUC stand for in keras metrics?

AUC stands for the Area Under the Curve and usually refers to the area under the receiver operator characteristic (ROC) curve. Learn more… Please help understand how accurate or reliable tf.keras.metrics.AUC metric is.

When can validation accuracy be greater than training accuracy?

If you are using data augmentation to “noisify” your training data, then it can make sense that you are getting better accuracy on the validation set, because it will be an easier dataset. If this is the case, then you don’t really have a problem. As a rule, your validation set should be as close as possible to your test set or real-life use case.

When does overfitting occur in a training set?

Overfitting happens when the performance on the training set is improved but the performance on the test data gets worse. What you got is consistent with overfitting but it could also be due to some other random difference between the training and test data.