Junior Developer Implementation Guide: Model-Based Fit Detectors (Issue #374)

For Beginners: What Are Model-Based Fit Detectors?
What EXISTS in src/FitDetectors/
What's MISSING (Test Coverage)
Step-by-Step Test Implementation
Testing Strategy
Common Pitfalls

For Beginners: What Are Model-Based Fit Detectors?

Advanced Model Analysis

Model-based fit detectors use sophisticated machine learning techniques to analyze fit:

Gaussian Process: Analyzes prediction uncertainty
- Provides confidence intervals for predictions
- Detects when model is uncertain vs. confidently wrong
Gradient Boosting: Uses ensemble learning insights
- Analyzes how many boosting iterations are needed
- Detects when additional models stop improving performance
Ensemble: Combines multiple detectors
- Voting or averaging across different detection methods
- More robust than single detector
Feature Importance: Analyzes which features matter
- Identifies irrelevant features (sign of overfitting)
- Detects if model depends too heavily on one feature
Calibrated Probability: Tests prediction confidence quality
- For classification: Are 90% confidence predictions actually right 90% of the time?
- Detects overconfident or underconfident models

What EXISTS in src/FitDetectors/

1. GaussianProcessFitDetector

Location: C:\Users\cheat\source\repos\AiDotNet\src\FitDetectors\GaussianProcessFitDetector.cs

What it does:

Performs Gaussian Process regression on evaluation data
Calculates mean predictions AND variance (uncertainty) predictions
Uses RBF (Radial Basis Function) kernel
Optimizes hyperparameters (length scale, noise variance) via grid search

Key Logic:

// Perform GP regression
var (meanPrediction, variancePrediction) = PerformGaussianProcessRegression(evalData);

var averageUncertainty = variancePrediction.Average();
var rmse = CalculateRMSE(actual, meanPrediction);

// Good fit: Low RMSE + Low uncertainty
if (rmse < GoodFitThreshold && averageUncertainty < LowUncertaintyThreshold)
    return FitType.GoodFit;

// Overfit: High RMSE + Low uncertainty (confidently wrong!)
if (rmse > OverfitThreshold && averageUncertainty < LowUncertaintyThreshold)
    return FitType.Overfit;

// Underfit: High RMSE + High uncertainty
if (rmse > UnderfitThreshold && averageUncertainty > HighUncertaintyThreshold)
    return FitType.Underfit;

return FitType.Unstable;

Gaussian Process Steps:

Optimize hyperparameters (length scale λ, noise variance σ²)
Calculate kernel matrix K using RBF kernel
Add noise to diagonal for stability
Perform Cholesky decomposition: K = L L^T
Solve for α: K α = y → α = K^(-1) y
Calculate mean predictions: μ* = K* α
Calculate variance predictions: σ*² = k** - v^T v

RBF Kernel:

k(x1, x2) = exp(-||x1 - x2||² / (2 λ²))

FitTypes Returned:

GoodFit (low error, low uncertainty)
Overfit (high error, low uncertainty - confidently wrong)
Underfit (high error, high uncertainty)
Unstable (mixed signals)

2. GradientBoostingFitDetector

Location: C:\Users\cheat\source\repos\AiDotNet\src\FitDetectors\GradientBoostingFitDetector.cs

What it does:

Analyzes learning curves from boosting iterations
Tracks training and validation error over iterations
Detects when validation error stops improving (plateau)
Identifies early stopping point

Key Concepts:

// Train gradient boosting, tracking error at each iteration
var trainingErrors = new List<T>();
var validationErrors = new List<T>();

for (int iter = 0; iter < maxIterations; iter++)
{
    // Add weak learner
    boosting.AddLearner();

    trainingErrors.Add(CalculateError(trainingSet));
    validationErrors.Add(CalculateError(validationSet));
}

// Analyze learning curves
var trainingErrorDecrease = trainingErrors[0] - trainingErrors[last];
var validationErrorDecrease = validationErrors[0] - validationErrors[last];

// Good fit: Both errors decrease together
if (trainingErrorDecrease > threshold && validationErrorDecrease > threshold)
    return FitType.GoodFit;

// Overfit: Training decreases, validation plateaus or increases
if (trainingErrorDecrease > threshold && validationErrorDecrease < smallThreshold)
    return FitType.Overfit;

// Underfit: Neither error decreases much
if (trainingErrorDecrease < smallThreshold && validationErrorDecrease < smallThreshold)
    return FitType.Underfit;

FitTypes Returned:

GoodFit (both errors decrease)
Overfit (training improves, validation doesn't)
Underfit (neither improves)

3. EnsembleFitDetector

Location: C:\Users\cheat\source\repos\AiDotNet\src\FitDetectors\EnsembleFitDetector.cs

What it does:

Combines multiple fit detectors
Uses voting or averaging to determine final FitType
More robust than any single detector

Key Logic:

// Create ensemble of detectors
var detectors = new IFitDetector<T, TInput, TOutput>[]
{
    new DefaultFitDetector<T, TInput, TOutput>(),
    new HoldoutValidationFitDetector<T, TInput, TOutput>(),
    new BootstrapFitDetector<T, TInput, TOutput>(),
    new GaussianProcessFitDetector<T, TInput, TOutput>()
};

// Get results from all detectors
var results = detectors.Select(d => d.DetectFit(evalData)).ToList();

// Voting strategy
var fitTypeCounts = results.GroupBy(r => r.FitType)
                          .OrderByDescending(g => g.Count())
                          .ToList();

var majorityFitType = fitTypeCounts.First().Key;
var consensusStrength = fitTypeCounts.First().Count() / (double)detectors.Length;

// Confidence based on consensus
var confidence = consensusStrength;  // e.g., 0.75 if 3/4 agree

Ensemble Strategies:

Majority Voting: Most common FitType
Weighted Voting: Weight by detector confidence
Unanimous: Only return FitType if all agree

FitTypes Returned: Any FitType (depends on ensemble)

4. FeatureImportanceFitDetector

Location: C:\Users\cheat\source\repos\AiDotNet\src\FitDetectors\FeatureImportanceFitDetector.cs

What it does:

Calculates importance of each feature
Uses permutation importance (shuffle feature, measure error increase)
Detects if model overfits to specific features

Key Logic:

// Calculate baseline error
var baselineError = CalculateError(model, X, y);

// For each feature
var importances = new List<double>();
foreach (int featureIndex in Enumerable.Range(0, numFeatures))
{
    // Shuffle this feature
    var XShuffled = ShuffleFeature(X, featureIndex);

    // Calculate new error
    var shuffledError = CalculateError(model, XShuffled, y);

    // Importance = how much error increased
    var importance = shuffledError - baselineError;
    importances.Add(importance);
}

// Analyze importance distribution
var maxImportance = importances.Max();
var avgImportance = importances.Average();
var importanceRatio = maxImportance / avgImportance;

// Overfit: Model depends heavily on few features
if (importanceRatio > OverfitThreshold)
    return FitType.Overfit;

// Underfit: All features have low importance
if (maxImportance < UnderfitThreshold)
    return FitType.Underfit;

// Good fit: Balanced importance across features
if (importanceRatio < GoodFitThreshold && maxImportance > MinThreshold)
    return FitType.GoodFit;

FitTypes Returned:

GoodFit (balanced feature importance)
Overfit (too dependent on few features)
Underfit (no features are important)
HighVariance (importance unstable)

5. CalibratedProbabilityFitDetector

Location: C:\Users\cheat\source\repos\AiDotNet\src\FitDetectors\CalibratedProbabilityFitDetector.cs

What it does:

Classification models only
Tests if predicted probabilities match actual outcomes
Uses calibration curves (reliability diagrams)
Calculates Expected Calibration Error (ECE)

Key Logic:

// Bin predictions by confidence level
var bins = CreateBins(numBins: 10);  // [0-0.1, 0.1-0.2, ..., 0.9-1.0]

foreach (var (predicted, actual) in predictions.Zip(actuals))
{
    var binIndex = (int)(predicted * numBins);
    bins[binIndex].Add((predicted, actual));
}

// Calculate calibration error for each bin
var calibrationErrors = new List<double>();
foreach (var bin in bins)
{
    var avgPredicted = bin.Average(p => p.predicted);  // Average confidence
    var avgActual = bin.Average(p => p.actual);        // Actual accuracy

    var error = |avgPredicted - avgActual|;
    calibrationErrors.Add(error);
}

// Expected Calibration Error
var ece = calibrationErrors.Average();

// Well-calibrated: ECE < threshold
if (ece < WellCalibratedThreshold)
    return FitType.GoodFit;

// Overconfident: Predictions higher than actual accuracy
if (avgPredicted > avgActual + threshold)
    return FitType.Overfit;

// Underconfident: Predictions lower than actual accuracy
if (avgPredicted < avgActual - threshold)
    return FitType.Underfit;

Calibration Curve:

X-axis: Predicted probability
Y-axis: Actual frequency of positive class
Perfect calibration: y = x (diagonal line)
Overconfident: Curve below diagonal
Underconfident: Curve above diagonal

FitTypes Returned:

GoodFit (well-calibrated)
Overfit (overconfident)
Underfit (underconfident)
Unstable (poor calibration)

What's MISSING (Test Coverage)

Current State

0% test coverage for all Model-Based FitDetectors
No tests for GP kernel calculations, Cholesky decomposition
No tests for feature permutation importance
No tests for calibration curves

Required Test Coverage

1. GaussianProcessFitDetector Tests

RBF kernel calculation: Verify against known values
Hyperparameter optimization: Grid search finds optimal values
Cholesky decomposition: Handles singular matrices
Mean/variance predictions: Match expected values
FitType logic: Low error + low uncertainty = GoodFit, etc.

2. GradientBoostingFitDetector Tests

Learning curves: Training and validation errors over iterations
Early stopping detection: Identifies when to stop
Overfit detection: Validation error increases while training decreases
Underfit detection: Neither error decreases

3. EnsembleFitDetector Tests

Voting mechanism: Majority vote works correctly
Consensus strength: Confidence reflects agreement level
All detectors agree: 100% confidence
Split decision: Lower confidence
Unanimous requirement: Works when configured

4. FeatureImportanceFitDetector Tests

Permutation importance: Shuffling reduces accuracy
Important vs. irrelevant features: Detected correctly
Overfit to one feature: High importance ratio
Balanced importance: GoodFit

5. CalibratedProbabilityFitDetector Tests

Perfect calibration: y = x diagonal
Overconfident model: Curve below diagonal
Underconfident model: Curve above diagonal
ECE calculation: Matches expected value
Binning strategy: Handles edge cases

Step-by-Step Test Implementation

Step 1: GaussianProcessFitDetector Tests

public class GaussianProcessFitDetectorTests
{
    [Fact]
    public void RBFKernel_IdenticalPoints_Returns1()
    {
        // Arrange
        var x = new Vector<double>(new[] { 1.0, 2.0, 3.0 });
        var lengthScale = 1.0;

        // Act
        var kernel = CalculateRBFKernel(x, x, lengthScale);

        // Assert: k(x, x) = exp(0) = 1
        Assert.Equal(1.0, kernel, precision: 6);
    }

    [Fact]
    public void RBFKernel_DistantPoints_ReturnsNearZero()
    {
        // Arrange
        var x1 = new Vector<double>(new[] { 0.0, 0.0 });
        var x2 = new Vector<double>(new[] { 10.0, 10.0 });
        var lengthScale = 1.0;

        // Act
        var kernel = CalculateRBFKernel(x1, x2, lengthScale);

        // Assert: exp(-||x1-x2||^2 / (2*λ^2)) ≈ 0 for large distance
        Assert.True(kernel < 0.01);
    }

    [Fact]
    public void DetectFit_LowErrorLowUncertainty_GoodFit()
    {
        // Arrange: Data that GP can fit well
        var detector = new GaussianProcessFitDetector<double, double[], double>();
        var evalData = CreateLinearDataWithNoise(noise: 0.01);

        // Act
        var result = detector.DetectFit(evalData);

        // Assert
        Assert.Equal(FitType.GoodFit, result.FitType);
    }

    [Fact]
    public void DetectFit_HighErrorLowUncertainty_Overfit()
    {
        // Arrange: GP is confident but wrong
        var detector = new GaussianProcessFitDetector<double, double[], double>();
        var evalData = CreateMismatchedData();  // Training and test from different distributions

        // Act
        var result = detector.DetectFit(evalData);

        // Assert
        Assert.Equal(FitType.Overfit, result.FitType);
    }

    [Fact]
    public void DetectFit_HighErrorHighUncertainty_Underfit()
    {
        // Arrange: Complex nonlinear data that GP struggles with
        var detector = new GaussianProcessFitDetector<double, double[], double>();
        var evalData = CreateComplexNonlinearData();

        // Act
        var result = detector.DetectFit(evalData);

        // Assert
        Assert.Equal(FitType.Underfit, result.FitType);
    }

    [Fact]
    public void HyperparameterOptimization_FindsReasonableValues()
    {
        // Arrange
        var detector = new GaussianProcessFitDetector<double, double[], double>();
        var evalData = CreateLinearDataWithNoise(noise: 0.1);

        // Act
        var result = detector.DetectFit(evalData);

        // Assert: Optimized hyperparameters should be reasonable
        // (Check via logging or reflection if exposed)
        Assert.NotNull(result);
    }

    [Fact]
    public void CholeskyDecomposition_SingularMatrix_HandlesGracefully()
    {
        // Arrange: Data that creates singular kernel matrix
        var detector = new GaussianProcessFitDetector<double, double[], double>();
        var evalData = CreateIdenticalPoints();  // All points the same

        // Act & Assert: Should not throw exception
        var exception = Record.Exception(() => detector.DetectFit(evalData));
        Assert.Null(exception);
    }

    [Fact]
    public void Recommendations_IncludeKernelSuggestions()
    {
        // Arrange
        var detector = new GaussianProcessFitDetector<double, double[], double>();
        var evalData = CreateLinearDataWithNoise();

        // Act
        var result = detector.DetectFit(evalData);

        // Assert: Should mention kernel functions
        var hasKernelMention = result.Recommendations.Any(
            r => r.Contains("kernel", StringComparison.OrdinalIgnoreCase));
        Assert.True(hasKernelMention);
    }
}

Step 2: FeatureImportanceFitDetector Tests

public class FeatureImportanceFitDetectorTests
{
    [Fact]
    public void DetectFit_OneImportantFeature_Overfit()
    {
        // Arrange: Model that only uses feature 0
        var detector = new FeatureImportanceFitDetector<double, double[], double>();
        var evalData = CreateDataWithOneImportantFeature();

        // Act
        var result = detector.DetectFit(evalData);

        // Assert: High importance ratio suggests overfitting
        Assert.Equal(FitType.Overfit, result.FitType);
    }

    [Fact]
    public void DetectFit_BalancedImportance_GoodFit()
    {
        // Arrange: All features contribute
        var detector = new FeatureImportanceFitDetector<double, double[], double>();
        var evalData = CreateDataWithBalancedFeatures();

        // Act
        var result = detector.DetectFit(evalData);

        // Assert
        Assert.Equal(FitType.GoodFit, result.FitType);
    }

    [Fact]
    public void DetectFit_NoImportantFeatures_Underfit()
    {
        // Arrange: All features have low importance
        var detector = new FeatureImportanceFitDetector<double, double[], double>();
        var evalData = CreateDataWithNoSignal();

        // Act
        var result = detector.DetectFit(evalData);

        // Assert
        Assert.Equal(FitType.Underfit, result.FitType);
    }

    [Fact]
    public void PermutationImportance_ImportantFeature_LargeDecrease()
    {
        // Arrange: Feature 0 is highly predictive
        var model = TrainModelOnFeature0();
        var X = CreateDataMatrix();
        var y = CreateTargets();

        // Act: Shuffle feature 0
        var baselineError = CalculateError(model, X, y);
        var XShuffled = ShuffleFeature(X, featureIndex: 0);
        var shuffledError = CalculateError(model, XShuffled, y);

        // Assert: Error should increase significantly
        Assert.True(shuffledError > baselineError * 1.5);  // At least 50% worse
    }

    [Fact]
    public void PermutationImportance_IrrelevantFeature_NoChange()
    {
        // Arrange: Feature 1 is not used by model
        var model = TrainModelOnFeature0();
        var X = CreateDataMatrix();
        var y = CreateTargets();

        // Act: Shuffle feature 1 (irrelevant)
        var baselineError = CalculateError(model, X, y);
        var XShuffled = ShuffleFeature(X, featureIndex: 1);
        var shuffledError = CalculateError(model, XShuffled, y);

        // Assert: Error should not change much
        Assert.InRange(shuffledError, baselineError * 0.95, baselineError * 1.05);
    }
}

Step 3: EnsembleFitDetector Tests

public class EnsembleFitDetectorTests
{
    [Fact]
    public void DetectFit_AllDetectorsAgree_HighConfidence()
    {
        // Arrange: Clear overfit scenario - all detectors should agree
        var detector = new EnsembleFitDetector<double, double[], double>();
        var evalData = FitDetectorTestHelpers.CreateMockEvaluationData(
            trainingR2: 0.98,
            validationR2: 0.55,
            testR2: 0.52);

        // Act
        var result = detector.DetectFit(evalData);

        // Assert
        Assert.Equal(FitType.Overfit, result.FitType);
        Assert.True(result.ConfidenceLevel >= 0.8);  // High consensus
    }

    [Fact]
    public void DetectFit_DetectorsDisagree_LowerConfidence()
    {
        // Arrange: Borderline case - detectors may disagree
        var detector = new EnsembleFitDetector<double, double[], double>();
        var evalData = FitDetectorTestHelpers.CreateMockEvaluationData(
            trainingR2: 0.75,
            validationR2: 0.72,
            testR2: 0.68);

        // Act
        var result = detector.DetectFit(evalData);

        // Assert: Confidence reflects disagreement
        Assert.NotNull(result.ConfidenceLevel);
        // (May be lower than unanimous case)
    }

    [Fact]
    public void Voting_MajorityWins()
    {
        // Arrange: 3 detectors say Overfit, 1 says GoodFit
        var detector = new EnsembleFitDetector<double, double[], double>(
            new EnsembleFitDetectorOptions { VotingStrategy = VotingStrategy.Majority });

        var evalData = CreateBorderlineOverfitData();

        // Act
        var result = detector.DetectFit(evalData);

        // Assert: Should pick majority (Overfit)
        Assert.Equal(FitType.Overfit, result.FitType);
    }

    [Fact]
    public void Voting_Unanimous_RequiresAllAgree()
    {
        // Arrange
        var detector = new EnsembleFitDetector<double, double[], double>(
            new EnsembleFitDetectorOptions { VotingStrategy = VotingStrategy.Unanimous });

        var evalData = CreateBorderlineData();  // Detectors may disagree

        // Act
        var result = detector.DetectFit(evalData);

        // Assert: If not unanimous, should return Unstable or lower confidence
        // (Implementation-dependent)
        Assert.NotNull(result);
    }
}

Step 4: CalibratedProbabilityFitDetector Tests

public class CalibratedProbabilityFitDetectorTests
{
    [Fact]
    public void DetectFit_PerfectCalibration_GoodFit()
    {
        // Arrange: Predicted probabilities match actual frequencies
        var detector = new CalibratedProbabilityFitDetector<double, double[], int>();

        // 90% confident predictions are right 90% of the time
        var (predicted, actual) = CreatePerfectlyCalib ratedPredictions();
        var evalData = CreateClassificationEvalData(predicted, actual);

        // Act
        var result = detector.DetectFit(evalData);

        // Assert
        Assert.Equal(FitType.GoodFit, result.FitType);
    }

    [Fact]
    public void DetectFit_Overconfident_Overfit()
    {
        // Arrange: Model predicts 90% confidence but only right 60% of time
        var detector = new CalibratedProbabilityFitDetector<double, double[], int>();

        var predicted = Enumerable.Repeat(0.9, 100).ToArray();
        var actual = GenerateActuals(trueRate: 0.6, count: 100);
        var evalData = CreateClassificationEvalData(predicted, actual);

        // Act
        var result = detector.DetectFit(evalData);

        // Assert
        Assert.Equal(FitType.Overfit, result.FitType);
    }

    [Fact]
    public void DetectFit_Underconfident_Underfit()
    {
        // Arrange: Model predicts 50% confidence but actually right 85% of time
        var detector = new CalibratedProbabilityFitDetector<double, double[], int>();

        var predicted = Enumerable.Repeat(0.5, 100).ToArray();
        var actual = GenerateActuals(trueRate: 0.85, count: 100);
        var evalData = CreateClassificationEvalData(predicted, actual);

        // Act
        var result = detector.DetectFit(evalData);

        // Assert
        Assert.Equal(FitType.Underfit, result.FitType);
    }

    [Fact]
    public void ECE_Calculation_MatchesExpected()
    {
        // Arrange: Known calibration curve
        var predicted = new[] { 0.1, 0.2, 0.3, 0.7, 0.8, 0.9 };
        var actual = new[] { 0, 0, 0, 1, 1, 1 };  // Binary

        // Expected: Bin [0-0.5]: avg_pred=0.2, avg_actual=0.0, error=0.2
        //           Bin [0.5-1.0]: avg_pred=0.8, avg_actual=1.0, error=0.2
        // ECE = (0.2 + 0.2) / 2 = 0.2

        // Act
        var ece = CalculateECE(predicted, actual, numBins: 2);

        // Assert
        Assert.Equal(0.2, ece, precision: 2);
    }

    [Fact]
    public void CalibrationCurve_DiagonalLine_PerfectCalibration()
    {
        // Arrange
        var predicted = Enumerable.Range(0, 10).Select(i => i / 10.0).ToArray();
        var actual = CreateActualsMatchingPredictions(predicted);

        // Act
        var (binCenters, binAccuracies) = CreateCalibrationCurve(predicted, actual);

        // Assert: Should form diagonal line (y ≈ x)
        for (int i = 0; i < binCenters.Length; i++)
        {
            Assert.Equal(binCenters[i], binAccuracies[i], precision: 1);
        }
    }
}

Testing Strategy

1. Mathematical Correctness

Verify kernel calculations against reference implementations
Test matrix operations (Cholesky, inverse) on known matrices
Compare calibration metrics to scikit-learn output

2. Model Training for Tests

Use simple synthetic datasets (linear, quadratic)
Train lightweight models for feature importance tests
Mock GP predictions if training is too slow

3. Performance Testing

GP with 100 points should complete in <5 seconds
Ensemble of 4 detectors should complete in <10 seconds
Feature importance with 10 features should complete in <3 seconds

4. Coverage Goals

Minimum: 80% code coverage
Target: 90% including all mathematical operations
Focus: All FitType branches, numerical stability

Common Pitfalls

1. Cholesky Decomposition Failure

Problem: Non-positive-definite matrix

Solution: Add small value to diagonal (jitter)

for (int i = 0; i < K.Rows; i++)
    K[i, i] += 1e-6;  // Numerical stability

2. GP Hyperparameter Optimization Slowness

Problem: Grid search over many parameters is slow

Solution:

Use coarse grid for tests (5x5 instead of 100x100)
Cache GP results for repeated tests
Use smaller datasets (n=50 instead of n=1000)

3. Feature Permutation Randomness

Problem: Tests fail intermittently due to random shuffling

Solution: Use fixed random seed

var random = new Random(seed: 42);

4. Calibration with Imbalanced Classes

Problem: Bins have very few samples

Solution: Use adaptive binning or minimum bin size

5. Ensemble Voting Ties

Problem: 2 detectors say Overfit, 2 say Underfit

Solution:

Implement tie-breaking rules
Use weighted voting by confidence
Return "Unstable" for ties

Next Steps

Review each detector's source code completely
Understand mathematical foundations (GP regression, calibration)
Create test data generators for each scenario
Implement comprehensive tests
Benchmark performance (ensure tests complete in reasonable time)
Document any numerical stability issues found

Additional Resources

Gaussian Process: http://gaussianprocess.org/
Feature Importance: https://christophm.github.io/interpretable-ml-book/
Model Calibration: https://scikit-learn.org/stable/modules/calibration.html
RBF Kernel: https://en.wikipedia.org/wiki/Radial_basis_function_kernel
Expected Calibration Error: https://arxiv.org/abs/1706.04599

Junior Developer Implementation Guide: Model-Based Fit Detectors (Issue #374)

Table of Contents

For Beginners: What Are Model-Based Fit Detectors?

Advanced Model Analysis

What EXISTS in src/FitDetectors/

1. GaussianProcessFitDetector

2. GradientBoostingFitDetector

3. EnsembleFitDetector

4. FeatureImportanceFitDetector

5. CalibratedProbabilityFitDetector

What's MISSING (Test Coverage)

Current State

Required Test Coverage

1. GaussianProcessFitDetector Tests

2. GradientBoostingFitDetector Tests

3. EnsembleFitDetector Tests

4. FeatureImportanceFitDetector Tests

5. CalibratedProbabilityFitDetector Tests

Step-by-Step Test Implementation

Step 1: GaussianProcessFitDetector Tests

Step 2: FeatureImportanceFitDetector Tests

Step 3: EnsembleFitDetector Tests

Step 4: CalibratedProbabilityFitDetector Tests

Testing Strategy

1. Mathematical Correctness

2. Model Training for Tests

3. Performance Testing

4. Coverage Goals

Common Pitfalls

1. Cholesky Decomposition Failure

2. GP Hyperparameter Optimization Slowness

3. Feature Permutation Randomness

4. Calibration with Imbalanced Classes

5. Ensemble Voting Ties

Next Steps

Additional Resources

Uh oh!

[Test Coverage] Implement Tests for Model-Based Fit Detectors #374

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