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CHANGELOG.md

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Changelog

  • v2.0.5

    • New: Added RobustLogisticRegression class with epsilon-contamination for outlier-resistant classification.
    • Enhanced: Poisson and Negative Binomial regression with empirical Fisher information matrix support.
    • Enhanced: Converted Negative Binomial from IWLS to proper Fisher scoring for consistency.
    • Added: Comprehensive offset support for Poisson regression rate modeling.
    • Fixed: Critical bugs in Negative Binomial prediction and standard error calculations.
    • Added: summary() and display_summary() methods with rich statistical output.
    • Validated: Mathematical correctness verified against statsmodels with machine precision accuracy.

  • v2.0.4

    • Added a beta version of Poisson and Negative Binomial regression using Fisher Scoring.
    • Changed naming conventions for simplicity and consistency.
    • Changed poetry to uv for packaging.

  • v2.0.3

    • Added a new functionality of inference of mean responses with confidence intervals for all algorithms.
    • Focal logistic regression now supports all model statistics, including standard errors, Wald statistics, p-values, and confidence intervals.

  • v2.0.2

    • Bug Fixes: Fixed the MultinomialLogisticRegression class to have flexible NumPy data types.

  • v2.0.1

    • Bug Fixes: Removed the debug print statement from the LogisticRegression class.

  • v2.0

    • Performance Improvements: Performance Enhancements: Optimized matrix calculations for substantial speed and memory efficiency improvements across all models. Leveraging streamlined operations, this version achieves up to 290x faster convergence. Performance gains per model:
      • Multinomial Logistic Regression: Training time reduced from 125.10s to 0.43s (~290x speedup).
      • Logistic Regression: Training time reduced from 0.24s to 0.05s (~5x speedup).
      • Focal Loss Logistic Regression: Training time reduced from 0.26s to 0.01s (~26x speedup).
    • Bug Fixes: verbose parameter in Focal Loss Logistic Regression now functions as expected, providing accurate logging during training.

  • v0.1.4

    • Updated log likelihood for Multinomial Regression and minor changes to Logistic Regression for integration with scikit-learn.

  • v0.1.3

    • Added coefficients, standard errors, p-values, and confidence intervals for Multinomial Regression.

  • v0.1.2

    • Updated NumPy dependency.

  • v0.1.1

    • Added support for Python 3.9+ 🐍.

  • v0.1.0

    • Initial release of Fisher Scoring Logistic, Multinomial, and Focal Loss Regression.