shap
augmented-interpretable-models
| shap | augmented-interpretable-models | |
|---|---|---|
| 1 | 1 | |
| 20,121 | 37 | |
| - | - | |
| 10.0 | 7.4 | |
| 8 months ago | 25 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
shap
-
Ethical and Bias Testing in Generative AI: A Practical Guide to Ensuring Ethical Conduct with Test Cases and Tools
Other tools like Fairness Indicators, Lime, and SHAP are also valuable resources for ethical and bias testing.
augmented-interpretable-models
-
[R] Emb-GAM: an Interpretable and Efficient Predictor using Pre-trained Language Models
Deep learning models have achieved impressive prediction performance but often sacrifice interpretability, a critical consideration in high-stakes domains such as healthcare or policymaking. In contrast, generalized additive models (GAMs) can maintain interpretability but often suffer from poor prediction performance due to their inability to effectively capture feature interactions. In this work, we aim to bridge this gap by using pre-trained neural language models to extract embeddings for each input before learning a linear model in the embedding space. The final model (which we call Emb-GAM) is a transparent, linear function of its input features and feature interactions. Leveraging the language model allows Emb-GAM to learn far fewer linear coefficients, model larger interactions, and generalize well to novel inputs (e.g. unseen ngrams in text). Across a variety of NLP datasets, Emb-GAM achieves strong prediction performance without sacrificing interpretability. All code is made available on Github.
What are some alternatives?
csgo-impact-rating - A probabilistic player rating system for Counter Strike: Global Offensive, powered by machine learning
language-planner - Official Code for "Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents"
transformers-interpret - Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
scikit-learn-ts - Powerful machine learning library for Node.js – uses Python's scikit-learn under the hood.
lime - Lime: Explaining the predictions of any machine learning classifier
DeepLearning - Contains all my works, references for deep learning
awesome-shapley-value - Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
handson-ml - ⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.
shap - A game theoretic approach to explain the output of any machine learning model.
gan-vae-pretrained-pytorch - Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
AutoCog - Automaton & Cognition