entropytree

A decision tree classifier written from scratch in pure Python (standard library only, no NumPy or other dependencies) using Shannon entropy and information gain.

pip install sai-entropytree

from entropytree import DecisionTree

X = [[2.0, 1.0], [3.0, 1.5], [6.0, 4.0], [7.0, 4.5], [8.0, 5.0]]
y = ["small", "small", "big", "big", "big"]

tree = DecisionTree(max_depth=2).fit(X, y)
print(tree.predict([[2.5, 1.2], [7.5, 4.8]]))   # ['small', 'big']
print(tree.score(X, y))                          # 1.0
tree.display(["length", "width"])

Hyperparameters

max_depth – size of the tree (default None = grow until pure)
min_samples_split – minimum samples needed to split a node
min_gain – minimum information gain to accept a split

API

fit(X, y), predict(X), score(X, y), get_depth(), export_text(feature_names), display(feature_names)

Metadata

Release files for sai-entropytree 0.2.0

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Uploaded via	`twine/6.2.0 CPython/3.9.6`

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