Add Gaussian Naive Bayes classifier for spot checking
Open
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 19
- Forks
- 6
- PR merge metrics
- No merged PRs in 30d
Description
We currently spot-check the following models:
- LogisticRegression
- DecisionTreeClassifier
- KNeighborsClassifier
- SVC
- RandomForestClassifier
- GradientBoostingClassifier
Let's add Gaussian Naive Bayes (GaussianNB) to the mix too.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Locate where the listed scikit-learn classifiers are assembled for spot checking, likely in the repository's notebooks or source. Add GaussianNB alongside the existing models and verify that the spot-check workflow includes it.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, python, scikit-learn
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100