presc 0.4.0

Performance Robustness Evaluation for Statistical Classifiers

PRESC: Performance and Robustness Evaluation for Statistical Classifiers

PRESC is a toolkit for the evaluation of machine learning classification models. Its goal is to provide insights into model performance which extend beyond standard scalar accuracy-based measures and into areas which tend to be underexplored in application, including:

• Generalizability of the model to unseen data for which the training set may not be representative
• Sensitivity to statistical error and methodological choices
• Performance evaluation localized to meaningful subsets of the feature space
• In-depth analysis of misclassifications and their distribution in the feature space

More details about the specific features we are considering are presented in the project roadmap. We believe that these evaluations are essential for developing confidence in the selection and tuning of machine learning models intended to address user needs, and are important prerequisites towards building trustworthy AI.

It also includes a package to carry out copies of machine learning classifiers.

As a tool, PRESC is intended for use by ML engineers to assist in the development and updating of models. It is usable in the following ways:

• As a standalone tool which produces a graphical report evaluating a given model and dataset
• As a Python package/API which can be integrated into an existing pipeline

A further goal is to use PRESC:

• As a step in a Continuous Integration workflow: evaluations run as a part of CI, for example, on regular model updates, and fail if metrics produce unacceptable values.

For the time being, the following are considered out of scope:

• User-facing evaluations, eg. explanations
• Evaluations which depend explicitly on domain context or value judgements of features, eg. protected demographic attributes. A domain expert could use PRESC to study misclassifications across such protected groups, say, but the PRESC evaluations themselves should be agnostic to such determinations.
• Analyses which do not involve the model, eg. class imbalance in the training data

There is a considerable body of recent academic research addressing these topics, as well as a number of open-source projects solving related problems. Where possible, we plan to offer integration with existing tools which align with our vision and goals.