# obliquetree
`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.
----
## Getting Started
`obliquetree` combines advanced capabilities with efficient performance. It supports **oblique splits**, leveraging **L-BFGS optimization** to determine the best linear weights for splits, ensuring both speed and accuracy.
In **traditional mode**, without oblique splits, `obliquetree` outperforms `scikit-learn` in terms of speed and adds support for **categorical variables**, providing a significant advantage over many traditional decision tree implementations.
When the **oblique feature** is enabled, `obliquetree` dynamically selects the optimal split type between oblique and traditional splits. If no weights can be found to reduce impurity, it defaults to an **axis-aligned split**, ensuring robustness and adaptability in various scenarios.
In very large trees (e.g., depth 10 or more), the performance of `obliquetree` may converge closely with **traditional trees**. The true strength of `obliquetree` lies in their ability to perform exceptionally well at **shallower depths**, offering improved generalization with fewer splits. Moreover, thanks to linear projections, `obliquetree` significantly outperform traditional trees when working with datasets that exhibit **linear relationships**.
-----
## Installation
To install `obliquetree`, use the following pip command:
```bash
pip install obliquetree
```
Using the `obliquetree` library is simple and intuitive. Here's a more generic example that works for both classification and regression:
```python
from obliquetree import Classifier, Regressor
# Initialize the model (Classifier or Regressor)
model = Classifier( # Replace "Classifier" with "Regressor" if performing regression
use_oblique=True, # Enable oblique splits
max_depth=2, # Set the maximum depth of the tree
n_pair=2, # Number of feature pairs for optimization
random_state=42, # Set a random state for reproducibility
categories=[0, 10, 32], # Specify which features are categorical
)
# Train the model on the training dataset
model.fit(X_train, y_train)
# Predict on the test dataset
y_pred = model.predict(X_test)
```
-----
## Documentation
For example usage, API details, comparisons with axis-aligned trees, and in-depth insights into the algorithmic foundation, we **strongly recommend** referring to the full [documentation](https://obliquetree.readthedocs.io/en/latest/).
---
## 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 the `n_pair` parameter, 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 assigns `NaN` values 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 to `scikit-learn`'s decision trees when oblique and categorical splitting are disabled.
- **Optimized Performance**
Outperforms `scikit-learn` in terms of speed and efficiency when oblique and categorical splitting are disabled:
- Up to **50% faster** for datasets with float columns.
- Up to **200% faster** for datasets with integer columns.
----
### Contributing
Contributions are welcome! If you'd like to improve `obliquetree` or suggest new features, feel free to fork the repository and submit a pull request.
-----
### License
`obliquetree` is released under the MIT License. See the LICENSE file for more details.
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"description": "# obliquetree\n\n`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.\n\n\n\n\n\n----\n\n## Getting Started\n\n`obliquetree` combines advanced capabilities with efficient performance. It supports **oblique splits**, leveraging **L-BFGS optimization** to determine the best linear weights for splits, ensuring both speed and accuracy.\n\nIn **traditional mode**, without oblique splits, `obliquetree` outperforms `scikit-learn` in terms of speed and adds support for **categorical variables**, providing a significant advantage over many traditional decision tree implementations.\n\nWhen the **oblique feature** is enabled, `obliquetree` dynamically selects the optimal split type between oblique and traditional splits. If no weights can be found to reduce impurity, it defaults to an **axis-aligned split**, ensuring robustness and adaptability in various scenarios.\n\nIn very large trees (e.g., depth 10 or more), the performance of `obliquetree` may converge closely with **traditional trees**. The true strength of `obliquetree` lies in their ability to perform exceptionally well at **shallower depths**, offering improved generalization with fewer splits. Moreover, thanks to linear projections, `obliquetree` significantly outperform traditional trees when working with datasets that exhibit **linear relationships**.\n\n-----\n## Installation\nTo install `obliquetree`, use the following pip command:\n```bash\npip install obliquetree\n```\n\nUsing the `obliquetree` library is simple and intuitive. Here's a more generic example that works for both classification and regression:\n\n\n```python\nfrom obliquetree import Classifier, Regressor\n\n# Initialize the model (Classifier or Regressor)\nmodel = Classifier( # Replace \"Classifier\" with \"Regressor\" if performing regression\n use_oblique=True, # Enable oblique splits\n max_depth=2, # Set the maximum depth of the tree\n n_pair=2, # Number of feature pairs for optimization\n random_state=42, # Set a random state for reproducibility\n categories=[0, 10, 32], # Specify which features are categorical\n)\n\n# Train the model on the training dataset\nmodel.fit(X_train, y_train)\n\n# Predict on the test dataset\ny_pred = model.predict(X_test)\n```\n-----\n\n## Documentation\nFor example usage, API details, comparisons with axis-aligned trees, and in-depth insights into the algorithmic foundation, we **strongly recommend** referring to the full [documentation](https://obliquetree.readthedocs.io/en/latest/).\n\n---\n## Key Features\n\n- **Oblique Splits** \n 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.\n\n- **Axis-Aligned Splits** \n Offers conventional (axis-aligned) splits, enabling users to leverage standard decision tree behavior for simplicity and interpretability.\n\n- **Feature Constraints** \n Limit the number of features used in oblique splits with the `n_pair` parameter, promoting simpler, more interpretable tree structures while retaining predictive power.\n\n- **Seamless Categorical Feature Handling** \n Natively supports categorical columns with minimal preprocessing. Only label encoding is required, removing the need for extensive data transformation.\n\n- **Robust Handling of Missing Values** \n Automatically assigns `NaN` values to the optimal leaf for axis-aligned splits.\n\n- **Customizable Tree Structures** \n The flexible API empowers users to design their own tree architectures easily.\n\n- **Exact Equivalence with `scikit-learn`** \n Guarantees results identical to `scikit-learn`'s decision trees when oblique and categorical splitting are disabled.\n\n- **Optimized Performance** \n Outperforms `scikit-learn` in terms of speed and efficiency when oblique and categorical splitting are disabled:\n - Up to **50% faster** for datasets with float columns.\n - Up to **200% faster** for datasets with integer columns.\n\n \n\n \n\n\n----\n### Contributing\nContributions are welcome! If you'd like to improve `obliquetree` or suggest new features, feel free to fork the repository and submit a pull request.\n\n-----\n### License\n`obliquetree` is released under the MIT License. See the LICENSE file for more details.\n",
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