creative-prediction
imodels
| creative-prediction | imodels | |
|---|---|---|
| 1 | 7 | |
| 23 | 1,293 | |
| - | - | |
| 0.0 | 8.5 | |
| over 1 year ago | 12 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | MIT License |
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creative-prediction
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?
CodeSearchNet - Datasets, tools, and benchmarks for representation learning of code.
pycaret - An open-source, low-code machine learning library in Python
vqgan-clip-generator - Implements VQGAN+CLIP for image and video generation, and style transfers, based on text and image prompts. Emphasis on ease-of-use, documentation, and smooth video creation.
interpret - Fit interpretable models. Explain blackbox machine learning.
rtdl - Research on Tabular Deep Learning (Python package & papers) [Moved to: https://github.com/Yura52/rtdl]
shap - A game theoretic approach to explain the output of any machine learning model.
edunn - Learn how modern, modular Neural Networks work by implementing a PyTorch/Keras-like framework.
linear-tree - A python library to build Model Trees with Linear Models at the leaves.
dopamine - Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
docarray - Represent, send, store and search multimodal data
AI-Art-Generator - A program that can add an artistic touch to any image.
Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera - Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes