Comparing Classifiers: A Case Study Using PyCM
This is an incremental tutorial for researchers and practitioners in machine learning, focusing on model evaluation tools.
The paper tackles the problem of selecting optimal classification models by demonstrating the PyCM library for deep-dive evaluations, showing that evaluation metrics can shift model efficacy interpretations and emphasizing the need for multi-dimensional frameworks to uncover subtle performance differences.
Selecting an optimal classification model requires a robust and comprehensive understanding of the performance of the model. This paper provides a tutorial on the PyCM library, demonstrating its utility in conducting deep-dive evaluations of multi-class classifiers. By examining two different case scenarios, we illustrate how the choice of evaluation metrics can fundamentally shift the interpretation of a model's efficacy. Our findings emphasize that a multi-dimensional evaluation framework is essential for uncovering small but important differences in model performance. However, standard metrics may miss these subtle performance trade-offs.