ROCit: Binary Classifier Performance Metrics and Visualization Package
by Md Riaz Ahmed Khan
Available on 1 platform
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Description
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.
This is a software package (R) for analysis, not a raw dataset; users must provide their own classification results as input.