v1.6.1

Fixed

• train subcommand now applies probability calibration (CalibratedClassifierCV) when calibrate_probabilities = true
• train subcommand now supports CV threshold method (TunedThresholdClassifierCV) in addition to OOF
• Reproducibility manifest now includes probability calibration parameters

Changed

• create-config template now includes threshold_objective, vme_cost, me_cost parameters
• validate-config summary table now displays probability calibration and threshold objective details

v1.6.0

Added

• Probability Calibration:

  • Optional post-hoc probability calibration on the best estimator per outer CV fold
  • Supports sigmoid (Platt scaling) and isotonic calibration methods
  • Applied after hyper-parameters tuning and before threshold tuning

• Calibration Diagnostics:

  • Brier Score: Mean squared error of probability predictions (lower is better)
  • ECE (Expected Calibration Error): Weighted average of calibration error across bins
  • MCE (Maximum Calibration Error): Maximum calibration error across any bin
  • Reliability curves (calibration plots) per outer CV fold and aggregate

• Repeated Stratified Cross-Validation:

  • outer_cv_repeats config option (default: 1)
  • Set >1 for repeated CV with different shuffles for more robust performance estimates

Changed

• metric_dict() now includes Brier Score, ECE, and MCE by default
• HTML report includes new calibration diagnostics section
• Output folder now includes calibration/ directory with reliability curve images

v1.5.1

Added

• OneHotEncoder min_frequency parameter to reduce noise from rare categorical values

Changed

• Updated requirements.txt with explicit version constraints for all dependencies
  • scikit-learn>=1.5.0 required for TunedThresholdClassifierCV

v1.5.0

Added

• VME/ME report:
  • VME (Very Major Error): Predicted susceptible when actually resistant
  • ME (Major Error): Predicted resistant when actually susceptible
• Flexible threshold objectives:
  • threshold_objective config option: youden (default), f1, f2, cost_sensitive
  • Cost-sensitive optimization with configurable vme_cost and me_cost weights
• Per-prediction uncertainty quantification to flag uncertain predictions near decision threshold
  • uncertainty_margin config option (default: 0.1) defines margin around threshold
  • Predictions within margin are flagged as uncertain in evaluation output
  • Uncertainty scores (0-1) provided for each prediction
• Reproducibility manifest (reproducibility.json) generated with run and train commands with environment info, data fingerprint, full configuration settings

Changed

• HTML report framework summary now displays threshold objective and cost weights (when applicable)
• Evaluation output now includes uncertainty and is_uncertain columns

v1.4.1

Changed

• Migrated documentation from MkDocs to Sphinx
• Documentation dependencies now loaded dynamically from docs-requirements.txt
• Development dependencies now loaded dynamically from dev-requirements.txt

v1.4.0

Added

• K-Nearest Neighbors (KNN) classifier support
• Missing data imputation with configurable methods:
  • SimpleImputer (mean, median, most_frequent strategies)
  • KNNImputer for k-nearest neighbors imputation
  • IterativeImputer with BayesianRidge or RandomForest estimator
• Comprehensive HTML report generation with metadata run and framework summary tables, results tables with 95% confidence intervals and confusion matrices
• Ruff linter integration in CI workflow for code quality

Changed

• Bootstrap confidence intervals now use sample-level predictions instead of fold-level metrics for more reliable statistical inference
• Updated CI workflow to include lint checks before tests
• Added Python 3.13 to CI test matrix

v1.3.1

Changed

• Reorganized package structure into sub-packages for clarity:
  • respredai/core/ - Pipeline, metrics, models, and ML utilities
  • respredai/io/ - Configuration and data handling
  • respredai/visualization/ - Plotting and visualization

Documentation

• Created docs/ structure with MkDocs

v1.3.0

Added

• train command for model training on entire dataset (cross-dataset validation)
• evaluate command to apply trained models to new data
• Automatic summary report after run command
• SHAP-based feature importance as fallback for models without native importance
  • Supports MLP, RBF_SVC, and TabPFN via KernelExplainer
  • --seed flag for reproducible SHAP computations

Documentation

• Added docs/train-command.md
• Added docs/evaluate-command.md
• Updated docs/feature-importance-command.md with SHAP fallback details

v1.2.0

Added

• validate-config command to validate configuration files without running the pipeline
  • Optional --check-data flag to also verify data file existence and column validity
• CLI override options for the run command: --models, --targets, --output, --seed
• CONTRIBUTING.md with development setup guide and contribution workflow

Changed

• Bootstrap confidence intervals in metrics output
• User-friendly error messages for missing config files or data paths

Documentation

• Added docs/validate-config-command.md
• Updated docs/run-command.md with CLI overrides section

v1.1.0

Added

• Threshold calibration with dual methods (OOF and CV) using Youden's J statistic
  • OOF method: Global optimization on concatenated out-of-fold predictions
  • CV method: Per-fold optimization with threshold averaging
  • Auto selection based on dataset size (n < 1000: OOF, otherwise: CV)
• Grouped cross-validation (StratifiedGroupKFold) to prevent data leakage in clinical datasets

Changed

• Expanded hyperparameter grids
• Enhanced CLI information display

Fixed

• XGBoost feature naming issue with special characters
• Color scheme in feature importance plots

Documentation

• Added comprehensive command documentation
  • docs/run-command.md
  • docs/create-config-command.md
  • docs/feature-importance-command.md
• Updated README with logo, quick start guide, and output structure