auto-sklearn
SMAC3
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| auto-sklearn | SMAC3 | |
|---|---|---|
| 3 | 2 | |
| 7,394 | 1,003 | |
| 0.7% | 3.4% | |
| 1.8 | 4.1 | |
| 4 months ago | 7 days ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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auto-sklearn
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Why not AutoML every tabular data?
Efficiency Ignoring the feature engineering aspects aside, a typical data scientist workflow involves trying out the different models. Some of the AutoML modules like H2O AutoML, AutoSklearn does this for you, and allow you to interpret your models. All these save so much time experimenting with the standard models.
- [R] Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data
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What free AutoML library do you recommend?
If you want a more stable AutoML library, i’ll suggest auto-sklearn which optimises performance of sklearn learning algorithms.
SMAC3
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[D]How to optimize an ANN?
You can use Optuna, SMAC or hyperopt
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Finding the optimal parameter
Apart from the aforementioned comments noting that this is an optimization problem, ready-to-use python libraries for this kind of problem (accounting for evaluation time) include http://hyperopt.github.io/hyperopt/, https://github.com/automl/SMAC3, or https://www.ray.io/ray-tune
What are some alternatives?
autogluon - AutoGluon: Fast and Accurate ML in 3 Lines of Code
hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
Auto-PyTorch - Automatic architecture search and hyperparameter optimization for PyTorch
optuna - A hyperparameter optimization framework
tune-sklearn - A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.
syne-tune - Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
optuna-examples - Examples for https://github.com/optuna/optuna
OCTIS - OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation