automl/auto-sklearn

Provide multi-class AUC ROC

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#1,482 opened on May 19, 2022

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 (8 comments) (0 reactions) (0 assignees)Python (7,270 stars) (1,265 forks)batch import
Good first issueNeed contributorenhancement

Description

Scikit-learn provides multi-class options for area under curve: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html

We should provide the most common ones, such as the OVO Macro averaging used by Auto-Gluon.

Contributor guide

Tech stack
python
Domain
machine learningdata
Issue type
feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
mostly clear
Prerequisites
Pythonscikit learn metricsAUC concepts
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
55
Research direction
The issue requests adding multi class AUC ROC support to auto sklearn, specifically OVO macro averaging as used in Auto Gluon. The body links to scikit learn's roc auc score documentation and Auto Gluon's implementation. To implement, study the scikit learn function and the Auto Gluon code referenced (https://github.com/awslabs/autogluon/blob/0b38dde5f698dbadfa1ce76aabda14505d9e3ead/core/src/autogluon/core/metrics/ init .py#L443). Then add a similar metric to auto sklearn's metrics module, ensuring compatibility with its evaluation framework.