Spearmint
yggdrasil-decision-forests
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| Spearmint | yggdrasil-decision-forests | |
|---|---|---|
| 2 | 4 | |
| 1,529 | 423 | |
| 0.1% | 4.0% | |
| 0.0 | 9.5 | |
| over 4 years ago | 6 days ago | |
| Python | C++ | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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Spearmint
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Why do tree-based models still outperform deep learning on tabular data?
It occurs to me that a system, trained on peer-reviewed applied-machine-learning literature and Kaggle winners, that generates candidates for structured feature-engineering specifications, based on plaintext descriptions of columns' real-world meaning, should be considered a requisite part of the "meta" here.
Ah, and then you could iterate within the resulting feature-engineering-suggestion space as a hyper-parameter between experiments, which could be optimized with e.g. https://github.com/HIPS/Spearmint . The papers write themselves!
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[D] What kind of Hyperparameter Optimisation do you use?
This was some time ago but I had some promising results with Bayesian optimization using a Gaussian Process prior. The method was developed by the guys who wrote Spearmint. That library doesn't support parallelization but I implemented the same technique in Scala without too much difficulty.
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?
optuna - A hyperparameter optimization framework
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
axe-testcafe - The helper for using Axe in TestCafe tests
decision-tree-classifier - Decision Tree Classifier and Boosted Random Forest
youtube-react - A Youtube clone built in React, Redux, Redux-saga
flashlight - A C++ standalone library for machine learning [Moved to: https://github.com/flashlight/flashlight]
decision-forests - A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
interpret - Fit interpretable models. Explain blackbox machine learning.
spaceopt - Hyperparameter optimization via gradient boosting regression