iiitl / iiitl/Logistic-Regression
Baseline logistic classifier with full metric set
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easy
good first issue
library
- Dominant language
- Jupyter Notebook
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- 0
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- 16
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Description
Train baseline Logistic Regression with stratified split and fixed seed.
Report Accuracy, Precision, Recall, F1, ROC-AUC, and confusion matrix on test data.
Contributor guide
Research direction
Start in the repository's Jupyter Notebook and locate the existing data-loading and model-training entry point. Add the baseline Logistic Regression workflow with a stratified split and fixed seed, then run the notebook and verify that test results include Accuracy, Precision, Recall, F1, ROC-AUC, and a confusion matrix.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 55/100