Python库 | Galaxy-ML-0.7.3.1.tar.gz

# 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.