contingency-space 0.1.1

A package for creating and analyzing contingency spaces.

Contingency Space

Abstract.

In Machine Learning, a supervised model's performance is measured using the verification metrics. In this study, we first present our motivation by revisiting the major limitations of these metrics, namely one-dimensionality, lack of context, lack of intuitiveness, uncomparability, binary restriction, and uncustomizability of metrics. In response, we propose Contingency Space, a bounded semimetric space that provides a generic representation for any performance verification metric. Then we showcase how this space addresses the limitations. In this space, each metric forms a surface using which we visually compare different verification metrics. Taking advantage of the fact that a metric's surface warps proportional to the degree of which it is sensitive to the class-imbalance ratio of data, we introduce Imbalance Sensitivity that quantifies the skew-sensitivity. Since an arbitrary model is represented in this space by a single point, we introduce Learning Path for qualitative and quantitative analyses of the training process. Using the semimetric that contingency space is endowed with, we introduce Tau as a new cost sensitive and Imbalance Agnostic metric. Lastly, we show that contingency space addresses multi-class problems as well. Throughout this work we define each concept through stipulated definitions and present every application with practical examples and visualizations.

How To Use.

• Use the notebooks under ./notebooks/ to explore some of the main functionalities.

• All metrics are available under ./metrics/.

• One of the notebooks (./notebooks/cnn_learning_path_on_MNIST.ipynb) requires the data of the learning paths. The data is pickled and available under ./pickled_data/.

Metadata.

• Python 3.12.5
• Tested on Ubuntu 20.04.2 LTS
• Utilized libraries are listed in ./requirements.txt.