tf-explain
decision-forests
| tf-explain | decision-forests | |
|---|---|---|
| 1 | 1 | |
| 1,007 | 651 | |
| 0.0% | 0.9% | |
| 0.0 | 8.3 | |
| almost 2 years ago | 11 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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tf-explain
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What alternative for explainable AI besides LIME do we have for deep learning models on images (keras)?
Have look at tf-explain and why not shap Shap?
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
What are some alternatives?
CleanTF2plus - Clean TF2's sequel
Spearmint - Spearmint Bayesian optimization codebase
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
yggdrasil-decision-forests - A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
jarconfig - combined scripts for tf2
srbench - A living benchmark framework for symbolic regression
pytest-visual - A visual testing framework for ML with automated change detection
higgs-logistic-regression
nhcustom - nhcustom is a program whose purpose is to modify the Team Fortress 2 mod "no-hats-bgum".
tfops-aug - TFOps-Aug: Implementation of policy-based image augmentation techniques based on TF2 Operations. All augmentations as efficient Tensorflow 2.11.0 operations. Easy integration into a tf.data API pipeline.