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Author:
Antonio Caparrini López, Javier Arroyo Gallardo
Summary:
mloptimizer is a Python library for optimizing hyperparameters of machine learning algorithms using genetic algorithms.
Latest version:
0.9.4
Required dependencies:
catboost
|
deap
|
joblib
|
kaleido
|
matplotlib
|
numpy
|
pandas
|
plotly
|
python-dateutil
|
pytz
|
scikit-learn
|
scipy
|
seaborn
|
six
|
tqdm
|
xgboost
Optional dependencies:
catboost
|
deap
|
hyperopt
|
joblib
|
kaleido
|
keras
|
lightgbm
|
matplotlib
|
mlflow
|
numpy
|
pandas
|
plotly
|
pydata-sphinx-theme
|
pytest-cov
|
pytest-mock
|
python-dateutil
|
pytz
|
scikit-learn
|
scipy
|
seaborn
|
six
|
sphinx-autoapi
|
sphinx-autodoc-typehints
|
sphinx-contributors
|
sphinx-favicon
|
sphinx-gallery
|
sphinx-github-changelog
|
sphinx_copybutton
|
sphinx_mdinclude
|
sphinxcontrib-mermaid
|
tensorflow
|
tqdm
|
ucimlrepo
|
xgboost
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