imodels
shap
Our great sponsors
imodels | shap | |
---|---|---|
7 | 38 | |
1,290 | 21,632 | |
- | 2.0% | |
8.5 | 9.3 | |
5 days ago | 1 day 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.
imodels
-
[D] Have researchers given up on traditional machine learning methods?
- all domains requiring high interpretability absolutely ignore deep learning at all, and put all their research into traditional ML; see e.g. counterfactual examples, important interpretability methods in finance, or rule-based learning, important in medical or law applications
-
What would be my best approach given the data I have?
Next, this variable will be your target and you can use various supervised learning models to answer your question. Since interpretation is key, you can use something from here: https://github.com/csinva/imodels or do some black box models and use shab to understand which features contributed most.
-
Random Forest Estimation Question
Option 2) fit a model from https://github.com/csinva/imodels on the predicted values of the RF
-
UC Berkeley Researchers Introduce ‘imodels: A Python Package For Fitting Interpretable Machine Learning Models
Despite recent breakthroughs in the formulation and fitting of interpretable models, implementations are frequently challenging to locate, utilize, and compare. imodels solves this void by offering a single interface and implementation for a wide range of state-of-the-art interpretable modeling techniques, especially rule-based methods. imodels is basically a Python tool for predictive modeling that is simple, transparent, and accurate. It gives users a straightforward way to fit and use state-of-the-art interpretable models, all of which are compatible with scikit-learn (Pedregosa et al., 2011). These models can frequently replace black-box models while boosting interpretability and computing efficiency without compromising forecast accuracy. Continue Reading
-
[D] Looking for open source projects to contribute
Our package imodels is expanding our sklearn-compatible set of interpretable models and always looking for new contributors!
- imodels: a package extending sklearn with state-of-the-art models for interpretable data science (e.g. Bayesian Rule Lists, RuleFit)
- imodels: a package extending sklearn with state-of-the-art interpretable models (e.g. Bayesian Rule Lists, RuleFit) from BAIR [P]
shap
- Shap v0.45.0
-
[D] Convert a ML model into a rule based system
something like GitHub - shap/shap: A game theoretic approach to explain the output of any machine learning model.?
-
[P] tinyshap: A minimal implementation of the SHAP algorithm
A less than 100 lines of code implementation of KernelSHAP because I had a hard time understanding shap's code.
-
What’s after model adequacy?
We use tools like SHAP to explain what the model is doing to stakeholders.
- Feature importance with feature engineering?
-
Model interpretation with many features
https://github.com/slundberg/shap this or https://github.com/marcotcr/lime would be relevant to you, especially if you want to look at explaining a single prediction.
-
SHAP Value Interpretation
See this closed topic for more detail: https://github.com/slundberg/shap/issues/29
-
Christoph Molnar on SHAP Library
Dr. Molnar recently had a semi-viral post on LinkedIn and on Twitter, where he essentially highlights the booming popularity [and power] of using SHAP for explainable AI (which I agree with), but that it also comes with problems; i.e., the open source implementation has thousands of pull requests, bugs, and issues and yet there is no permanent or significant funding to go in and fix them.
-
Random Forest Estimation Question
Option 4) create SHAP values https://github.com/slundberg/shap to better understand what the RF did.
-
Model explainability
txtai pipelines are wrappers around Hugging Face pipelines with logic to easily integrate with txtai's workflow framework. Given that, we can use the SHAP library to explain predictions.
What are some alternatives?
pycaret - An open-source, low-code machine learning library in Python
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
interpret - Fit interpretable models. Explain blackbox machine learning.
Transformer-Explainability - [CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
linear-tree - A python library to build Model Trees with Linear Models at the leaves.
captum - Model interpretability and understanding for PyTorch
docarray - Represent, send, store and search multimodal data
lime - Lime: Explaining the predictions of any machine learning classifier
Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera - Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes
dopamine - Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
awesome-production-machine-learning - A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning