[contour-decision-boundary] Decision Boundary Classifier Visualization

[MarkusNeusinger]

A decision boundary visualization showing classifier regions in 2D feature space. Colors indicate predicted class regions, with training points overlaid to show classification accuracy.

[claude]

✅ Spec ID: contour-decision-boundary

Rationale: Uses filled contour regions to visualize how a classifier partitions 2D feature space, distinct from general contour plots as it's specifically for ML classification evaluation.

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[github-actions]

✅ Specification Ready: contour-decision-boundary

PR: #2933

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specification.md

contour-decision-boundary: Decision Boundary Classifier Visualization

Description

A decision boundary visualization showing how a classifier partitions 2D feature space into predicted class regions. Colored regions indicate the predicted class at each point in the feature space, with training data points overlaid to show how well the classifier separates different classes. This visualization is essential for understanding classifier behavior, identifying decision boundaries, and evaluating classification accuracy in machine learning.

Applications

• Evaluating the performance of classification algorithms (SVM, KNN, logistic regression, decision trees) on 2D data
• Teaching machine learning concepts by visualizing how different classifiers create decision boundaries
• Comparing classifier complexity and potential overfitting by examining boundary smoothness
• Debugging classification models by identifying misclassified training samples

Data

• X1 (numeric) - First feature dimension, continuous values
• X2 (numeric) - Second feature dimension, continuous values
• y (categorical) - Class labels for each training point (typically 2-5 classes)
• Size: 50-500 training points, with a dense mesh grid (100x100 to 200x200) for boundary visualization
• Example: Synthetic classification data (make_moons, make_circles, make_blobs) or reduced real-world features

Notes

• Use distinct colors for each class region with appropriate transparency
• Overlay training points with markers showing true class labels
• Consider using different marker styles to highlight correctly vs incorrectly classified points
• Include a legend showing class labels
• A trained classifier (sklearn or similar) is required to generate predictions on the mesh grid

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