entropytree
A decision tree classifier written from scratch (NumPy only) using Shannon entropy and information gain.
pip install sai-entropytree
from sklearn.datasets import load_iris
from entropytree import DecisionTree
iris = load_iris()
tree = DecisionTree(max_depth=2).fit(iris.data, iris.target)
print(tree.score(iris.data, iris.target))
tree.display(iris.feature_names)
Hyperparameters
max_depth– size of the tree (defaultNone= grow until pure)min_samples_split– minimum samples needed to split a nodemin_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)
1 maintainer
Metadata
1 maintainer
Release files for sai-entropytree 0.1.0
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