🎨 Decision Boundary Playground

Compare decision boundaries for Logistic Regression, SVM (linear/RBF), kNN, and Decision Tree on classic datasets. Click on the canvas to add points, adjust hyperparameters, and watch the boundary change live.

Visualization Controls Guide
Dataset & Decision Boundary
Class 1 Class 0
Boundary is shaded toward predicted class; darker means stronger confidence. Dashed line (when applicable) shows estimated boundary contour.
Accuracy
—
Click to add data points. Use the right panel to switch dataset/classifier and adjust hyperparameters.
📦 Dataset
🤖 Classifier
LR0.10
Reg (C)1.0
Epochs/Step20
Logistic requires training steps. Press Train Step after changing LR/C/epochs.
ℹ️ Notes
Logistic and linear SVM are linear separators; RBF SVM bends the boundary using kernel features. kNN adapts to local neighborhoods; trees carve axis-aligned regions.
How to use & What to observe
Quick start
  1. Click a dataset (Blobs/Moons/Circles) → Generate.
  2. Pick a classifier. For Logistic/SVM press Train Step after changing sliders. kNN/Tree update immediately.
  3. Watch the boundary color: green/red regions = predicted class; darker = higher confidence.
  4. Click on the canvas to add points and see the model adapt.
What to compare
  • Linear vs non‑linear: Circles with Linear SVM vs RBF SVM.
  • Local vs global: kNN (local) vs Logistic/SVM (global) on Moons.
  • Simplicity vs fit: Tree depth small vs large on Blobs.
Troubleshooting
  • No change on Train Step? Generate data first, then train.
  • Boundary unchanged? Increase epochs or learning rate (Logistic/SVM) or adjust C/Gamma.
  • Too jagged (overfit)? Increase k (kNN) or decrease depth (Tree).
Goal: build intuition for how algorithms shape decision boundaries and how hyperparameters change that shape.

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