DTOR: Decision Tree Oulier Regressor for outlier explanations

DTOR (Decision Tree Outlier Regressor) is a Python library for explaining outlier detection results using decision trees. It provides a method to interpret the decisions made by an outlier detection model, specifically tailored for anomaly detection tasks.

Authors & contributors:

Riccardo Crupi, Daniele Regoli, Alessandro Damiano Sabatino, Immacolata Marano, Massimiliano Brinis, Luca Albertazzi, Andrea Cirillo, Andrea Claudio Cosentini

To know more about this research work, please refer to our paper ArXiv.

Overview

DTOR aims to improve the interpretability of outlier detection models, particularly in scenarios where understanding the reasons behind a data point being flagged as an outlier is crucial. It offers methods to extract decision rules from an ensemble of decision trees trained to distinguish outliers from normal data points.

Key Features

• Explainability for outlier detection models
• Decision tree-based interpretation
• Support for both local and global explanations
• Customizable parameters for decision tree construction

Usage

Here's a basic example demonstrating how to use DTOLR to explain outlier predictions:

from dtor import DTOR

# Initialize DTOR with your training data, outlier predictions, and the outlier detection model
dtor = DTOR(X_train, y_pred_train, clf_ad)

# Explain instances
explanations = dtor.explain_instances(X_expl, y_pred_expl)

# Print the explanations
print(explanations)

Contributing

Contributions to DTOR are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request on GitHub.