Is a Classification Procedure Good Enough? A Goodness-of-Fit Assessment Tool for Classification Learning

TL;DR

This paper introduces BAGofT, a new goodness-of-fit test for classification methods that does not rely on parametric assumptions, effectively detecting underfitting in both parametric and non-parametric models.

Contribution

The paper proposes BAGofT, a novel data-splitting methodology for assessing the fit of any classification procedure, addressing a gap in existing goodness-of-fit testing methods.

Findings

BAGofT effectively detects underfitting in classification models.

The method controls test size and has high power with increasing sample size.

Simulation studies demonstrate its advantages over existing methods.

Abstract

In recent years, many non-traditional classification methods, such as Random Forest, Boosting, and neural network, have been widely used in applications. Their performance is typically measured in terms of classification accuracy. While the classification error rate and the like are important, they do not address a fundamental question: Is the classification method underfitted? To our best knowledge, there is no existing method that can assess the goodness-of-fit of a general classification procedure. Indeed, the lack of a parametric assumption makes it challenging to construct proper tests. To overcome this difficulty, we propose a methodology called BAGofT that splits the data into a training set and a validation set. First, the classification procedure to assess is applied to the training set, which is also used to adaptively find a data grouping that reveals the most severe regions of underfitting. Then, based on this grouping, we calculate a test statistic by comparing the estimated success probabilities and the actual observed responses from the validation set. The data splitting guarantees that the size of the test is controlled under the null hypothesis, and the power of the test goes to one as the sample size increases under the alternative hypothesis. For testing parametric classification models, the BAGofT has a broader scope than the existing methods since it is not restricted to specific parametric models (e.g., logistic regression). Extensive simulation studies show the utility of the BAGofT when assessing general classification procedures and its strengths over some existing methods when testing parametric classification models.