Schematex
decisiontree·CART / scikit-learn plot_tree convention·research, education·complexity 3/3·since v0.2.0

Iris CART decision tree

Machine-learning decision tree for the classic Iris classifier with split thresholds, sample counts, Gini impurity, and class leaves.

For the data scientist

Open in Playground →
decisiontree:ml·§ —
↘ preview
100%
Iris CART Classifier Decision tree (ml mode) with 7 nodes and 6 edges Iris CART Classifier True False True False True False petal_length 2.45 gini = 0.67 samples = 150 Setosa gini = 0 samples = 50 value = 50 class = setosa petal_width 1.75 gini = 0.50 samples = 100 petal_length 4.95 gini = 0.17 samples = 54 Versicolor gini = 0.04 samples = 49 value = 49 class = versicolor Virginica gini = 0.32 samples = 5 value = 5 class = virginica Virginica gini = 0 samples = 45 value = 45 class = virginica
UTF-8 · LF · 12 lines · 697 chars✓ parsed·3.7 ms·7.8 KB SVG

Scenario

Model explainability often starts with a small tree. This Iris example mirrors the structure exported by CART tools: feature thresholds inside split nodes, impurity metrics, samples, and class-labelled leaves.

Annotation key

Decision tree syntax →