Interpretable Contextual-Accountable and Responsible Machine
Learning
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Documentation for package ‘icarm’ version 0.1.0
Help Pages
| icarm_audit | Generate a JSON audit trail |
| icarm_calibrate | Probability calibration diagnostics |
| icarm_compare | Compare multiple icarm_models |
| icarm_equalized_odds_curve | Equalized odds curves across thresholds |
| icarm_equity_summary | Equity summary from a fairness report |
| icarm_explain | Generate global model explanations |
| icarm_explain_local | Local explanation for individual observations |
| icarm_fairness | Group-level fairness audit |
| icarm_financial | Synthetic Financial Loan Default Dataset |
| icarm_fit | Fit an ICARM model on any tabular data |
| icarm_medical | Synthetic Medical Readmission Dataset |
| icarm_metrics | Compute performance metrics for any task |
| icarm_plot_calibration | Plot calibration curve |
| icarm_plot_comparison | Plot multi-model comparison |
| icarm_plot_confusion | Plot confusion matrix |
| icarm_plot_fairness | Plot group-level fairness metric |
| icarm_plot_importance | Plot feature importance |
| icarm_plot_roc_groups | Plot per-group ROC curves |
| icarm_plot_thresholds | Plot threshold performance curves |
| icarm_racism_survey | Synthetic Racism and Civic Participation Survey |
| icarm_scorecard | Generate a full accountability scorecard |
| icarm_split | Reproducible train/test split |
| icarm_thresholds | Threshold sweep for binary classification |
| predict.icarm_model | Predict from an icarm_model |
| print.icarm_model | Print an icarm_model |
| summary.icarm_model | Summary of an icarm_model |