ROCit: Binary Classifier Performance Metrics and Visualization Package

ROCit is a software package for evaluating binary classifiers, created by Md Riaz Ahmed Khan. It calculates threshold-bound metrics like sensitivity, precision, and accuracy, and provides tools for generating ROC curves, AUC statistics, and confidence intervals using empirical, binormal, and non-parametric methods. The package also includes visualization options for ROC curves, KS plots, and lift plots, and features an empirical gains table for direct marketing applications.

Use Cases

• Evaluate overall classifier performance based on area under the ROC curve (AUC) summary statistic.
• Calculate threshold-specific metrics like precision and recall for model tuning.
• Construct confidence intervals for ROC curves and AUC to assess metric reliability.
• Generate empirical gains tables for direct marketing campaign analysis.
• Create visualizations like ROC curves, KS plots, and lift plots for model interpretation.

Strengths

• Package offers multiple methods for ROC curve and AUC calculation, including empirical, binormal, and non-parametric.
• Includes functionality for constructing confidence intervals for ROC curves and AUC.
• Provides visualization options for ROC curves, KS plots, and lift plots with options for manual customization.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Md Riaz Ahmed Khan

Collection Method

Software package for performance calculation and visualization.

Time Range

Temporal coverage is unknown.

Freshness

Last updated date is unknown.

Geography

Spatial coverage is unknown.