Introduction
obliquetree is an advanced decision tree implementation designed to provide high-performance and interpretable models. It supports both classification and regression tasks, enabling a wide range of applications. By offering traditional and oblique splits, it ensures flexibility and improved generalization with shallow trees. This makes it a powerful alternative to regular decision trees.
Installation
To install obliquetree, use the following pip command:
pip install obliquetree
Key Features
Oblique Splits
Perform oblique splits using linear combinations of features to capture complex patterns in data. Supports both linear and soft decision tree objectives for flexible and accurate modeling.Axis-Aligned Splits
Offers conventional (axis-aligned) splits, enabling users to leverage standard decision tree behavior for simplicity and interpretability.Feature Constraints
Limit the number of features used in oblique splits with then_pairparameter, promoting simpler, more interpretable tree structures while retaining predictive power.Seamless Categorical Feature Handling
Natively supports categorical columns with minimal preprocessing. Only label encoding is required, removing the need for extensive data transformation.Robust Handling of Missing Values
Automatically assignsNaNvalues to the optimal leaf for axis-aligned splits.Customizable Tree Structures
The flexible API empowers users to design their own tree architectures easily.Exact Equivalence with
scikit-learn
Guarantees results identical toscikit-learn’s decision trees when oblique and categorical splitting are disabled.Parallel Split Search Utilizes OpenMP-based parallelism during tree construction to evaluate splits in parallel across features, enabling significant speedups on multi-core systems.
Optimized Performance Outperforms
scikit-learnin terms of speed and efficiency when oblique and categorical splitting are disabled:Up to 600% faster for datasets with float columns.
Up to 400% faster for datasets with integer columns.
