decision-forests
yggdrasil-decision-forests
| decision-forests | yggdrasil-decision-forests | |
|---|---|---|
| 1 | 4 | |
| 651 | 428 | |
| 0.8% | 3.0% | |
| 8.3 | 9.5 | |
| 9 days ago | 3 days ago | |
| Python | C++ | |
| Apache License 2.0 | Apache License 2.0 |
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decision-forests
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Why do tree-based models still outperform deep learning on tabular data?
I can't explain it, but I help maintain TensorFlow Decision Forests [1] and Yggdrasil Decision Forests [2], and in an AutoML system at work that trains models on lots of various users data, decision forest models gets selected as best (after AutoML tries various model types and hyperparameters) somewhere between 20% to 40% of the times, systematically. It's pretty interesting. Other ML types considered are NN, linear models (with auto feature crossings generation), and a couple of other variations.
[1] https://github.com/tensorflow/decision-forests
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 ?
What are some alternatives?
Spearmint - Spearmint Bayesian optimization codebase
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.
srbench - A living benchmark framework for symbolic regression
tensorflow - An Open Source Machine Learning Framework for Everyone
higgs-logistic-regression
decision-tree-classifier - Decision Tree Classifier and Boosted Random Forest
flashlight - A C++ standalone library for machine learning [Moved to: https://github.com/flashlight/flashlight]
interpret - Fit interpretable models. Explain blackbox machine learning.
MLBenchmarks.jl - ML models benchmarks on public dataset