CategoricalNB

Repository metrics

Stars

4,574 stars (4,574 stars)

PR merge metrics

Avg merge 47d 6h (Avg merge 47d 6h)46 merged PRs in 30d (46 merged PRs in 30d)

Description

Contributor guide

Research direction

Examine the scikit learn CategoricalNB implementation and the referenced discussion to understand requirements, then implement the CategoricalNB class in river following its API conventions and code style.

Tech stack

python

Domain

machine learning

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.

Clear

Prerequisites

Pythonscikit learn basics

Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.

60