ML.NET.Classification.Metrics 0.1.4

ML.NET Classification Metrics: ROC Curve & AUC

About

ML.NET.Classification.Metrics is an unofficial library for ML.NET that adds detailed ROC curve generation and AUC calculation, complementing ML.NET’s built-in metrics with richer visualization and clearer threshold analysis.

Key Features

• ROC curve (Exact): Generates ROC points for every unique score threshold, with tied scores grouped, plus endpoints, giving the most accurate ROC curve and AUC.
• ROC curve (Binned): Sweeps thresholds from 1.0 to 0.0 using (buckets + 1) steps for a fast, fixed-size approximation (best when inputs are probabilities in [0,1])
• Custom buckets: Choose bucket count in Binned mode to trade curve detail for speed.
• Consistent AUC: Computes AUC via trapezoidal integration over the same ROC points, so the curve and AUC always match.
• Threshold visibility: Each ROC point includes its threshold, making cutoff analysis (TPR/FPR changes) straightforward.
• Interop-ready: Returns strongly-typed .NET structs, making results easy to visualize in charts and diagnostics tools.

Library Guide

EvaluateRoc builds a ROC curve by sorting samples by score and simulating different decision thresholds, expecting:

• scores (float[]): a ranking signal where higher values mean “more likely positive”
• labels (bool[]): ground truth labels (true = positive, false = negative)

Important: scores[] and labels[] must be aligned (same row order, same length).

• Use Probability when available (calibrated in [0,1]). Otherwise use Score and set clampScoresToUnitInterval: false.

Logistic Regression: prepare scores and labels (ensure the correct label column name)

public sealed class BinaryPrediction
{
    public float Probability { get; set; } // Logistic Regression: in [0,1]
}

public sealed class LabelRow
{
    public bool Label { get; set; }
}

var predictions = model.Transform(testData);

float[] scores = mlContext.Data
    .CreateEnumerable<BinaryPrediction>(predictions, reuseRowObject: false)
    .Select(r => r.Probability)
    .ToArray();

bool[] labels = mlContext.Data
    .CreateEnumerable<LabelRow>(testData, reuseRowObject: false)
    .Select(r => r.Label)
    .ToArray();

Example A: Exact ROC (recommended)

• Use Exact mode for the most accurate ROC curve and AUC (one point per unique threshold + endpoints).

  var rocExact = CoreMetricsEvaluator.EvaluateRoc( scores: scores, labels: labels, mode: RocMode.Exact, clampScoresToUnitInterval: true );

double auc = rocExact.Score; var points = rocExact.Points; // ROC points

Example B: Binned ROC (fast approximation)

• Use Binned mode for a fast, fixed-size ROC curve: thresholds sweep from 1.0 → 0.0 using (buckets + 1) steps.

• Binned mode assumes scores are in [0,1] (or set clampScoresToUnitInterval: true).

  var rocBinned = CoreMetricsEvaluator.EvaluateRoc( scores: scores, labels: labels, mode: RocMode.Binned, buckets: 200, clampScoresToUnitInterval: true );

EvaluateRoc returns a MetricsCurve<RocPoint>:

• roc.Score → the AUC

• roc.Points → ROC points containing:

  • Fpr (False Positive Rate)
  • Tpr (True Positive Rate)
  • Threshold (the cutoff used for that point)

NuGet package

1. Add the NuGet package:

• dotnet add package ML.NET.Classification.Metrics --version 0.1.2

2. Import the namespace in your code

• using ML.NET.Metrics.Evaluation.Public;

Libraries

• .NET 8.0
• Windows x64 native binary