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Decision Tree written in Rust, with Python bindings
Extremely slow Decision Tree written in Rust
Have you found yourself yerning for a slower version of Scikit-Learn's
DecisionTreeClassifier with less features?
You've come to the right place.
This was a weekend project and I've botched the implementation somewhere, probably in the splitter logic for tree nodes, so while it's written entirely in Rust and requires no dependencies; it manages to do a lot of inefficient logic, very quickly.
On a handful of testing data, with equivelent parameters, it yields the same results, only much, much slower.
This is currently the first release and the tree only implements
fit(X, y) and
predict(X) methods as of now,
and only as a classifier (no regression tree yet)
Maybe I'll get back to fixing it, maybe not.
pip install --upgrade pyrus-decision-tree
pip uninstall pyrus-decision-tree
from pyrus_decision_tree import PyrusDecisionTree dataset = [[2.771244718, 1.7847839292], [1.728571309, 1.1697614132], [3.678319846, 2.812813571], [3.961043357, 2.619950321], [2.999208922, 2.209014212], [7.497545867, 3.162953546], [9.00220326, 3.339047188], [7.444542326, 0.476683375], [10.12493903, 3.234550982], [6.642287351, 3.319983761]] targets = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1] clf = PyrusDecisionTree(5) clf.fit(dataset, targets) predictions = clf.predict(dataset)
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