LicketySPLIT
LicketySPLIT is a Python package with a C++ backend for learning sparse decision trees for classification and regression. It builds on the LicketySPLIT algorithm from Near Optimal Decision Trees in a SPLIT Second and generalizes it with recursive pilot ideas as in From Rashomon Theory to PRAXIS: Efficient Decision Tree Rashomon Sets. For continuous features, we recommend the included binarization techniques from Fast Sparse Decision Tree Optimization via Reference Ensembles, which preserve optimality with respect to a gradient-boosted tree ensemble.
The package supports binary and multi-class classification, regression, optional sample weights, efficient subproblem caching, and threshold-based binarization for continuous features.
See the example notebook here.
Objective
LicketySPLIT learns a sparse decision tree by attempting to minimize a regularized empirical training objective.
For an unweighted classification dataset with n training samples, the objective is
objective(tree) = training_mistakes(tree) + lambda_leaf * n * number_of_leaves(tree)
where:
training_mistakes(tree)is the number of training samples misclassified by the treenumber_of_leaves(tree)is the number of leaves in the treelambda_leafis the leaf regularization parameternis the number of training samples
For a weighted dataset, n is replaced with the sum of weights and the training_mistakes(tree) penalizes incorrect points by their weight instead of by 1.
For a regression dataset, training_mistakes(tree) is replaced with the sum of squared between the target values and the tree predictions. Our leaves predict a constant value: the (possibly weighted) mean of the training sample target values in that leaf.
Classification and Regression specifics
Labels should be encoded as contiguous nonnegative integers:
0, 1, 2, ..., num_classes - 1
For regression, targets should be numeric values.
Features
LicketySPLIT expects binary input features. For continuous data, use the included ThresholdGuessBinarizer to generate binary threshold features.
from licketysplit import ThresholdGuessBinarizer
binarizer = ThresholdGuessBinarizer(
learning_rate=0.1,
n_estimators=100,
max_depth=3,
random_state=0,
column_elimination=False,
)
X_train_bin = binarizer.fit_transform(X_train_raw, y_train)
X_test_bin = binarizer.transform(X_test_raw)
feature_names = binarizer.get_feature_names_out()
Each generated feature has the form
original_feature <= threshold
Full example
A complete notebook showing loading data, binarization, fitting, prediction, tree inspection, plotting, caching, and sample weights is available here.
References
This package builds on the LicketySPLIT algorithm from:
Near Optimal Decision Trees in a SPLIT Second
and generalizes it with a pilot algorithm approach as in:
From Rashomon Theory to PRAXIS: Efficient Decision Tree Rashomon Sets
For continuous-feature binarization, we recommend:
Fast Sparse Decision Tree Optimization via Reference Ensembles
1 maintainer
Metadata
1 maintainer
Release files for licketysplit 0.2.0
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| Tags | CPython 3.9 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64 |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
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Transparency logRelease files / licketysplit-0.2.0-cp39-cp39-macosx_11_0_arm64.whl
| Download URL | licketysplit-0.2.0-cp39-cp39-macosx_11_0_arm64.whl |
|---|---|
| Size | 202.5 kB |
| Tags | CPython 3.9 macOS 11.0+ ARM64 |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
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Transparency logRelease files / licketysplit-0.2.0-cp39-cp39-macosx_10_9_x86_64.whl
| Download URL | licketysplit-0.2.0-cp39-cp39-macosx_10_9_x86_64.whl |
|---|---|
| Size | 206.4 kB |
| Tags | CPython 3.9 macOS 10.9+ x86-64 |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
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