Metadata-Version: 2.1
Name: Galaxy-ML
Version: 0.7.3
Summary: Galaxy Machine Learning Library
Home-page: https://github.com/goeckslab/Galaxy-ML/
License: UNKNOWN
Description: 
        # Galaxy-ML
        Galaxy-ML is a web machine learning end-to-end pipeline building framework, with special support to biomedical data. Under the management of unified scikit-learn APIs, cutting-edge machine learning libraries are combined together to provide thousands of different pipelines suitable for various needs. In the form of [Galalxy](https://github.com/galaxyproject/galaxy) tools, Galaxy-ML provides scalabe, reproducible and transparent machine learning computations.
        
        
        ### Key features
        -  simple web UI
        -  no coding or minimum coding requirement
        -  fast model deployment and model selection, specialized in hyperparameter tuning using `GridSearchCV`
        -  high level of parallel and automated computation
        
        
        ### Supported modules
        A typic machine learning pipeline is composed of a main estimator/model and optional preprocessing component(s).
        
        ##### Model
        - _[scikit-learn](https://github.com/scikit-learn/scikit-learn)_
            - sklearn.ensemble
            - sklearn.linear_model
            - sklearn.naive_bayes
            - sklearn.neighbors
            - sklearn.svm
            - sklearn.tree
        - _[xgboost](https://github.com/dmlc/xgboost)_
            - XGBClassifier
            - XGBRegressor
        - _[mlxtend](https://github.com/rasbt/mlxtend)_
            - StackingCVClassifier
            - StackingClassifier
            - StackingCVRegressor
            - StackingRegressor
        - _[keras](https://github.com/keras-team/keras)_
            - KerasGClassifier (new API)
            - KerasGRegressor (new API)
          
        ##### Preprocessor
        - _[scikit-learn](https://github.com/scikit-learn/scikit-learn)_
            - sklearn.preprocessing
            - sklearn.feature_selection
            - sklearn.decomposition
            - sklearn.kernel_approximation
            - sklearn.cluster
        - _[imblanced-learn](https://github.com/scikit-learn-contrib/imbalanced-learn)_
            - imblearn.under_sampling
            - imblearn.over_sampling
            - imblearn.combine
        - _[skrebate](https://github.com/EpistasisLab/scikit-rebate/tree/master/skrebate)_
            - ReliefF
            - SURF
            - SURFstar
            - MultiSURF
            - MultiSURFstar
          
        ##### Custom implementations for biomedical application
        - [IRAPSClassifier](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5445594/)
        - BinarizeTargetClassifier/BinarizeTargetRegressor
        - [TDMScaler](https://www.ncbi.nlm.nih.gov/pubmed/26844019)
        - DyRFE/DyRFECV
        - Z_RandomOverSampler
        
        ### Examples
        1. Build a simple randomforest model.
        
Platform: any
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Provides-Extra: docs
