mllp
Hierarchical rules
An implementation of a hierarchical rule-based model for transparent and interpretable classification tasks using multilayer logical perceptrons.
The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
22 stars
4 watching
6 forks
Language: Python
last commit: about 1 year ago
Linked from 2 awesome lists
aaaiexplainabilityexplainable-aiexplainable-mlimlinterpretabilityinterpretable-aiinterpretable-machine-learninginterpretable-mlinterpretmlmachine-learningmachine-learning-interpretabilityrule-basedrule-setstransparencytransparent-mlxai
Related projects:
Repository | Description | Stars |
---|---|---|
| Teaching software developers how to build transparent and explainable machine learning models using Python | 673 |
| Provides tools and techniques for interpreting machine learning models | 483 |
| A framework for building and evaluating machine learning systems with high accuracy and interpretability, particularly in human-centered applications. | 13 |
| An Explainable AI toolbox that provides various methods and tools to understand and interpret the behavior of neural networks | 654 |
| Provides counterfactual explanations for machine learning models to facilitate interpretability and understanding. | 1,373 |
| A Python package implementing an interpretable machine learning model for text classification with visualization tools | 336 |
| Provides an implementation of Hierarchical explanations for Neural Network predictions | 127 |
| An eXplainability toolbox for machine learning that enables data analysis and model evaluation to mitigate biases and improve performance | 1,135 |
| An R package and workshop materials for explaining machine learning models using explainable AI techniques | 52 |
| An asynchronous engine for continuous and autonomous machine learning | 26 |
| An online repository providing resources and information on explainable AI, algorithmic fairness, ML security, and related topics | 107 |
| A scikit-learn wrapper for interpretable classifiers based on decision rules | 489 |
| A toolkit for explaining complex AI models and data-driven insights | 1,641 |
| Provides methods to interpret and explain the behavior of machine learning models | 494 |
| An implementation of a neural network architecture for document classification using hierarchical attention mechanisms | 87 |