Decision Tree

What it does

Builds a tree of simple rules, with readable output and feature importance.

Use it when an explainable set of rules is useful.

Data you need

• Target
• Numeric features

Supported data: Numeric, Categorical. Level: Intermediate.

Formula and method

Gini = 1 − Σpₖ² · Entropy = −Σpₖ log₂ pₖ

The tree repeatedly picks the feature and threshold that most reduce Gini impurity, entropy or squared error, until it reaches your limits. The rules are printed so they can be read and checked.

How to read the result

Follow the rules from the root to a leaf.

Worked example

Illustrative run on Fisher iris measurements. These figures are the actual output of this model on that example, and they say nothing about your own data.

• Test accuracy = 0.9211 against a majority-class baseline of 0.3421; F1 = 0.923.
• The model is better than always predicting the most common class on this test set.

Limitations

• Deep trees overfit

Numerical validation

Validated 14 of 14 automated checks passed, including numerical results, invalid input, missing values, small samples, constant variables, charts and the interpretation text.

Computed with python 3.13.2, numpy 2.2.5, scipy 1.14.1, pandas 2.3.3, statsmodels 0.14.4, scikit-learn 1.7.0, in your browser. A passing check confirms the listed comparison only. It does not make any result suitable for your data, and it is not a claim of equivalence with other statistical software.

Questions

How deep should a tree be?

Deep enough to capture patterns, and shallow enough to generalize. Compare training and test performance.