Interactive Decision Tree Classifier Sandbox

Red Prediction Space

Blue Prediction Space

Splitting Splits

Confusion Matrix (Training Set Evaluation)

Predicted Red (+)

Predicted Blue (-)

Total

Actual Red

0 0.0% True Red (TP)

0 0.0% False Blue (FN)

Actual Blue

0 0.0% False Red (FP)

0 0.0% True Blue (TN)

Total

🌳 Hierarchical Tree Structure Flowchart

1. Select Class to Plot

Class +1 (Red) Class -1 (Blue)

2. Maximum Growth Depth (Tree Depth)

Max Depth Bound3

Shallow depths create broad generalized stumps (underfitting). High growth bounds create intricate checkerboards wrapping individual coordinates (overfitting).

3. Select Target Focus Class for Metrics Calculations

Red Class (+) Blue Class (-)

Derived Evaluation Metrics Calculations

Accuracy

0.0%

Precision

0.0%

Sensitivity (Recall)

0.0%

Specificity

0.0%

Model Status: Optimization Active
Plot discrete elements on the coordinate plane to watch recursive axis splits form in real-time.

🔬 Classroom Experiment: Load the quadrant sample and push Depth up to 5. Look at how perfectly straight vertical and horizontal grid partitions are drawn across the coordinate landscape. This highlights why decision trees are orthogonal space cutters!