rasbt/mlxtend

Add option for termination criterion with tolerance

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#1,079 opened on Jan 18, 2024

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 (1 comment) (2 reactions) (0 assignees)Python (911 forks)batch import
New Featureeasyhelp wanted

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Description

Describe the workflow you want to enable

Add an option for a termination criterion similar to "tol" implemented in sklearn.feature_selection.SequentialFeatureSelector: https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SequentialFeatureSelector.html

Describe alternatives you've considered, if relevant

While the sklearn SequentialFeatureSelector can do this, it misses the history callback you get in this package, which i think is very nice and helpful.

Additional context

The version of mlxtend i am using is 0.23.1

Contributor guide

Research direction
Examine mlxtend's sequential feature selector.py file, specifically the SequentialFeatureSelector class. Implement a tolerance parameter that stops feature selection when the performance improvement is below the threshold, similar to sklearn's implementation (https://scikit learn.org/stable/modules/generated/sklearn.feature selection.SequentialFeatureSelector.html). Ensure the history callback continues to work with the new criterion. No existing PRs or comments provide additional guidance.
Tech stack
pythonnumpypandas
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.
Fresh
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Mostly clear
Prerequisites
knowledge of mlxtend's SequentialFeatureSelectorfamiliarity with sklearn's tol parameterbasic Python
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
60