yggdrasil-decision-forests
interpret
| yggdrasil-decision-forests | interpret | |
|---|---|---|
| 4 | 6 | |
| 428 | 6,007 | |
| 3.0% | 0.6% | |
| 9.5 | 9.7 | |
| 5 days ago | 3 days ago | |
| C++ | C++ | |
| Apache License 2.0 | MIT License |
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yggdrasil-decision-forests
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Why do tree-based models still outperform deep learning on tabular data? (2022)
Is it this library https://github.com/google/yggdrasil-decision-forests ?
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Binary image classification using random forest algorithm
However if you know cpp you can use Yggdrasil https://github.com/google/yggdrasil-decision-forests.
- Why do tree-based models still outperform deep learning on tabular data?
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[P] Tree compiler that speeds up LightGBM model inference by ~30x
Have you tried to compare with Yggdrasil, the decision forest engine (c++, both training and inference) powering TensorFlow Decision Forests ?
interpret
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[D] Alternatives to the shap explainability package
Maybe InterpretML? It's developed and maintained by Microsoft Research and consolidates a lot of different explainability methods.
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What Are the Most Important Statistical Ideas of the Past 50 Years?
You may also find Explainable Boosting Machines interesting: https://github.com/interpretml/interpret
They're a bit like a best of both worlds between linear models and random forests (generalized additive models fit with boosted decision trees)
Disclosure: I helped build this open source package
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[N] Google confirms DeepMind Health Streams project has been killed off
Microsoft Explainable Boosting Machine (which is a Gaussian Additive Model and not a Gradient Boosted Trees 🙄 model) is a step in that direction https://github.com/interpretml/interpret
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[Discussion] XGBoost is the way.
Also I'd recommend everyone who works with xgboost to give EBM's a try! They perform comparably (except in the case of extreme interactions) but are actually interpretable! https://github.com/interpretml/interpret/ Beside that they since on runtime they're practically a lookup table they're very quick (at the cost of longer training time).
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[D] Generalized Additive Models… with trees?
Open source code by Microsoft: https://github.com/interpretml/interpret (called EBM in this implementation).
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Machine Learning with Medical Data (unbalanced dataset)
If it's not an image, have a go at Microsoft's Explainable Boosting Maching) https://github.com/interpretml/interpret which is not a GBM but a GAM (Gradient Boosting Machine vs Gradient Additive Model). This will also give you explanation via SHAP or LIME values.
What are some alternatives?
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
shap - A game theoretic approach to explain the output of any machine learning model.
tensorflow - An Open Source Machine Learning Framework for Everyone
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
decision-tree-classifier - Decision Tree Classifier and Boosted Random Forest
alibi - Algorithms for explaining machine learning models
flashlight - A C++ standalone library for machine learning [Moved to: https://github.com/flashlight/flashlight]
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Spearmint - Spearmint Bayesian optimization codebase
medspacy - Library for clinical NLP with spaCy.
srbench - A living benchmark framework for symbolic regression