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 →
schematex§ source-defined
Iris CART Classifier Decision tree (ml mode) with 7 nodes and 6 edges Iris CART Classifier setosa versicolor virginica True False True False True False Petal length ≤ 2.45 150 samples gini 0.67 SETOSA Setosa 50 samples gini 0.00 value = 50 Petal width ≤ 1.75 100 samples gini 0.50 Petal length ≤ 4.95 54 samples gini 0.17 VERSICOLOR Versicolor 49 samples gini 0.04 value = 49 VIRGINICA Virginica 5 samples gini 0.32 value = 5 VIRGINICA Virginica 45 samples gini 0.00 value = 45
12 lines · 697 chars
✓ parsed·0.0 ms·0 B
100%

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 →