Assume a data set that includes examples of a class Non-default and examples of class Default. Assume further that you’re evaluating your model’s performance to predict examples of class Non-default.False positives is a field in the confusion...

Assume a data set that includes examples of a class Non-default and examples of class Default. Assume further that you’re evaluating your model’s performance to predict examples of class Non-default.True negatives is a field in the confusion...

Assume a data set that includes examples of a class Non-default and examples of class Default. Assume further that you’re evaluating your model’s performance to predict examples of class Non-default.True positives is a field in the confusion...

A confusion matrix helps to illustrate what kinds of errors a classification model is making.
If you have a binary classifier model that distinguishes between a positive and a negative class, you can define the following 4 values depending on the...

The Kolmogorov–Smirnov test (KS) is a statistical test to check that two data samples come from the same distribution. The advantage of this test is that it’s non-parametric in nature, therefore it is distribution agnostic. Thus, we are not concerned...

The mean absolute error (MAE) is an average of the absolute errors. The MAE units are the same as the predicted target, which is useful for understanding whether the size of the error is of concern or not. The smaller the MAE the better the model’s...