iiitl / iiitl/Logistic-Regression
Solver, regularization, and class_weight selection with CV
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hard
library
- Dominant language
- Jupyter Notebook
- Stars
- 0
- Forks
- 16
- PR merge metrics
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Description
Compare multiple solvers and C values with class_weight options using cross-validation.
Choose the best configuration and report final holdout metrics and confusion matrix.
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
Use the repository's Jupyter Notebook as the entry point and locate the current logistic-regression evaluation workflow. Compare solvers, C values, and class_weight settings with cross-validation; done means selecting the best configuration and reporting final holdout metrics with a confusion matrix.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, scikit-learn
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100