nltk/nltk

How to stabilize NaiveBayesClassifier outputs?

Open

#2,097 opened on Aug 28, 2018

View on GitHub
 (7 comments) (0 reactions) (0 assignees)Python (2,826 forks)batch import
good first issuetaggertests

Repository metrics

Stars
 (12,712 stars)
PR merge metrics
 (Avg merge 8d 5h) (88 merged PRs in 30d)

Description

On sometimes we see that the continuous integration test fails because of some non-deterministic results from the NgramTagger i.e. are NaiveBayesClassifer, e.g. https://nltk.ci.cloudbees.com/job/pull_request_tests/822/PYV=3.6.4,jdk=jdk8latestOnlineInstall/testReport/junit/(root)/nltk/tag/

Is there someway to set a random seed or something such that classifier's output is consistent and make sure that the model is easily replicable?

Contributor guide

Research direction
Investigate the source of non determinism in NaiveBayesClassifier (likely due to random initialization) and propose adding a random seed parameter to ensure reproducible outputs.
Tech stack
python
Domain
machine learning
Issue type
Bug
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
2
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonGit
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
70