auto-sklearn
tune-sklearn
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| auto-sklearn | tune-sklearn | |
|---|---|---|
| 3 | 4 | |
| 7,394 | 462 | |
| 0.7% | - | |
| 1.8 | 0.0 | |
| 4 months ago | 6 months ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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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.
tune-sklearn
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LightGBM vs. XGBoost: Which distributed version is faster?
Of course not! :)
The Ray ecosystem is actually chalk full of integrations, from XGBoost Ray (https://docs.ray.io/en/master/xgboost-ray.html), to PyTorch on Ray (https://docs.ray.io/en/master/using-ray-with-pytorch.html), and of course hyperparameter search with Ray Tune for a variety of libraries, including Sklearn (https://github.com/ray-project/tune-sklearn).
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[D] I'm new and scrappy. What tips do you have for better logging and documentation when training or hyperparameter training?
If you mainly use scikit-learn, you should consider using tune-sklearn.
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[P] Bayesian Hyperparameter Optimization with tune-sklearn in PyCaret
Just wanted to share a not widely known feature of PyCaret. By default, PyCaret's tune_model uses the tried and tested RandomizedSearchCV from scikit-learn. However, not everyone knows about the various advanced options tune_model() currently allows you to use such as cutting edge hyperparameter tuning techniques like Bayesian Optimization through libraries such as tune-sklearn, Hyperopt, and Optuna.
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[D] Here are 3 ways to Speed Up Scikit-Learn - Any suggestions?
You might want to try out tune-sklearn as it seems like it works for catboost as well. I am trying it use tune-sklearn to speed up my scikit-learn hyperparameter tuning.
What are some alternatives?
autogluon - AutoGluon: Fast and Accurate ML in 3 Lines of Code
guildai - Experiment tracking, ML developer tools
Auto-PyTorch - Automatic architecture search and hyperparameter optimization for PyTorch
hummingbird - Hummingbird compiles trained ML models into tensor computation for faster inference.
syne-tune - Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
dvc - 🦉 ML Experiments and Data Management with Git
OCTIS - OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
spock - spock is a framework that helps manage complex parameter configurations during research and development of Python applications
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
SMAC3 - SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
pymarl2 - Fine-tuned MARL algorithms on SMAC (100% win rates on most scenarios)