imodels
ProSelfLC-AT
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| imodels | ProSelfLC-AT | |
|---|---|---|
| 7 | 4 | |
| 1,290 | 57 | |
| - | - | |
| 8.5 | 1.8 | |
| 5 days ago | over 1 year ago | |
| Jupyter Notebook | HTML | |
| MIT License | GNU General Public License v3.0 or later |
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
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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]
ProSelfLC-AT
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[R] Robust Learning: the past and present. The DNN has strong fitting capability, but we find ...
Found relevant code at https://github.com/XinshaoAmosWang/ProSelfLC-AT + all code implementations here
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[R] ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State
Code for https://arxiv.org/abs/2207.00118 found: https://github.com/XinshaoAmosWang/ProSelfLC-AT
- [P] Easy to install, use, extend, run experiments and sink results: PyTorch Implementation for ProSelfLC-CVPR 2021
- [R] CVPR 2021-Progressive Self Label Correction (ProSelfLC) for Training Robust Deep Neural Networks
What are some alternatives?
pycaret - An open-source, low-code machine learning library in Python
Data-Efficient-Reinforcement-Learning-with-Probabilistic-Model-Predictive-Control - Unofficial Implementation of the paper "Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control", applied to gym environments
interpret - Fit interpretable models. Explain blackbox machine learning.
Improving-Mean-Absolute-Error-against-CCE - Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitudeās Variance Matters
shap - A game theoretic approach to explain the output of any machine learning model.
romodel - Modeling robust optimization problems in Pyomo
linear-tree - A python library to build Model Trees with Linear Models at the leaves.
ProSelfLC - noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation. [Moved to: https://github.com/XinshaoAmosWang/ProSelfLC-AT]
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