DiCE
Generate Diverse Counterfactual Explanations for any machine learning model. (by interpretml)
imodels
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible). (by csinva)
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| DiCE | imodels | |
|---|---|---|
| 2 | 7 | |
| 1,270 | 1,290 | |
| 2.1% | - | |
| 8.2 | 8.5 | |
| 10 days ago | 6 days ago | |
| Python | Jupyter Notebook | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
DiCE
Posts with mentions or reviews of DiCE.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-31.
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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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[R] The Shapley Value in Machine Learning
Counter-factual and recourse-based explanations are alternative approach to model explanations. I used to work in a large financial institution, and we were researching whether counter-factual explanation methods would lead to better reason codes for adverse action notices.
imodels
Posts with mentions or reviews of imodels.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-31.
-
[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?
When comparing DiCE and imodels you can also consider the following projects:
OmniXAI - OmniXAI: A Library for eXplainable AI
pycaret - An open-source, low-code machine learning library in Python
CARLA - CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
interpret - Fit interpretable models. Explain blackbox machine learning.
AIX360 - Interpretability and explainability of data and machine learning models
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.
harakiri - Help applications kill themselves
docarray - Represent, send, store and search multimodal data
stranger - Chat anonymously with a randomly chosen stranger
Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera - Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes