xplainable
imodels
| xplainable | imodels | |
|---|---|---|
| 2 | 7 | |
| 52 | 1,293 | |
| - | - | |
| 9.0 | 8.5 | |
| 14 days ago | 17 days ago | |
| Python | Jupyter Notebook | |
| GNU Affero General Public License v3.0 | MIT License |
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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.
xplainable
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Explainable (Structured) Machine Learning Algorithm
Just for some respite from the discussion of our soon-to-be AI overlords (LLMs), I'm one of the contributors to an open-source Python package, Xplainable (https://github.com/xplainable/xplainable). Xplainable is a novel (structured) machine learning algorithm that's inherently explainable, as opposed to being a post-hoc explainer (like SHAP or Lime).
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Tools for documenting OS Python Package
I'm looking at migrating the docs for our open-source Python package https://github.com/xplainable/xplainable from sphinx to something else. I was initially looking at either docosaurus or Mintlify. Mintlify looks substantially easier to setup but I'm questioning the extensibility (also the cost).
imodels
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[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
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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.
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Random Forest Estimation Question
Option 2) fit a model from https://github.com/csinva/imodels on the predicted values of the RF
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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
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[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]
What are some alternatives?
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
pycaret - An open-source, low-code machine learning library in Python
interpret - Fit interpretable models. Explain blackbox machine learning.
shap - A game theoretic approach to explain the output of any machine learning model.
linear-tree - A python library to build Model Trees with Linear Models at the leaves.
docarray - Represent, send, store and search multimodal data
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.
Network-Intrusion-Detection-Using-Machine-Learning - A Novel Statistical Analysis and Autoencoder Driven Intelligent Intrusion Detection Approach
intro-to-python - [READ-ONLY MIRROR] An intro to Python & programming for wanna-be data scientists
ANN-decompiler - "AI" demystified: a decompiler
nn - 🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠