What a boundary plot shows
A decision boundary is the surface where a classifier switches its predicted class. Plotting it over the feature space reveals the shape the model carved out and where it will struggle.
How to draw one
- Build a dense grid of points covering the feature plane.
- Ask the model to predict each grid point.
- Color regions by predicted class and overlay the real data.
This only works directly in two dimensions, so high dimensional data is projected or reduced first.
What it reveals
- A linear model shows straight boundaries; a tree shows boxy ones.
- Overly wiggly regions hint at overfitting to noise.
- Misclassified points sitting deep in the wrong region flag hard cases.
Cautions
- A two dimensional view of many features can mislead.
- The grid resolution affects how smooth the picture looks.
- Use it for intuition, then confirm with held out metrics.
Key idea
Visualizing the decision boundary colors the feature space by predicted class, exposing model shape and overfitting. It is an intuition tool best confirmed by metrics.