imodels
linear-tree
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| imodels | linear-tree | |
|---|---|---|
| 7 | 3 | |
| 1,290 | 323 | |
| - | - | |
| 8.5 | 0.0 | |
| 5 days ago | 11 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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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 āimodels: 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]
linear-tree
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Is there any algorithm that combines decision trees with regression models?
Sure is! Hereās an implementation
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Running a randomforest on residuals from a ridge linear model in scikit learn
Check out https://github.com/cerlymarco/linear-tree
- GitHub - cerlymarco/linear-tree: A python library to build Model Trees with Linear Models at the leaves.
What are some alternatives?
pycaret - An open-source, low-code machine learning library in Python
eli5 - A library for debugging/inspecting machine learning classifiers and explaining their predictions
interpret - Fit interpretable models. Explain blackbox machine learning.
python-machine-learning-book-3rd-edition - The "Python Machine Learning (3rd edition)" book code repository
shap - A game theoretic approach to explain the output of any machine learning model.
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
docarray - Represent, send, store and search multimodal data
dtreeviz - A python library for decision tree visualization and model interpretation.
Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera - Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes
ML-Workspace - š All-in-one web-based IDE specialized for machine learning and data science.
dopamine - Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
BigFivePersonality - The current project provides a Machine Learning trained model that is able to classify the trait with maximum value of the Big Five Personality Test, given the answers of this one.