iterative-stratification
auto-sklearn
| iterative-stratification | auto-sklearn | |
|---|---|---|
| 1 | 3 | |
| 817 | 7,409 | |
| - | 0.4% | |
| 0.0 | 1.8 | |
| almost 2 years ago | 4 months ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
iterative-stratification
-
TypeError: unhashable type: 'list' when preparing index of labels for MultiLabelBinarizer
I need to create this so I can encode the Labels and run iterative stratification as detailed [here](https://github.com/trent-b/iterative-stratification). Once I have the index prepared, i will run MultiLabelBinarizer to encode the "Labels" list and create a matrix of those values. I will then run the stratification sampling algorithm on that matrix to determine zero-based train and test indices. The code I have below is causing an error.
auto-sklearn
-
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
-
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.
What are some alternatives?
datatap-python - Focus on Algorithm Design, Not on Data Wrangling
autogluon - Fast and Accurate ML in 3 Lines of Code
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
Auto-PyTorch - Automatic architecture search and hyperparameter optimization for PyTorch
Dask - Parallel computing with task scheduling
tune-sklearn - A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.
timebasedcv - Time based splits for cross validation
syne-tune - Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
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.
SMAC3 - SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
pymarl2 - Fine-tuned MARL algorithms on SMAC (100% win rates on most scenarios)