Galaxy-ML 0.7.3

Galaxy Machine Learning Library

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 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
  • sklearn.ensemble
  • sklearn.linear_model
  • sklearn.naive_bayes
  • sklearn.neighbors
  • sklearn.svm
  • sklearn.tree
• xgboost
  • XGBClassifier
  • XGBRegressor
• mlxtend
  • StackingCVClassifier
  • StackingClassifier
  • StackingCVRegressor
  • StackingRegressor
• keras
  • KerasGClassifier (new API)
  • KerasGRegressor (new API)

Preprocessor

• scikit-learn
  • sklearn.preprocessing
  • sklearn.feature_selection
  • sklearn.decomposition
  • sklearn.kernel_approximation
  • sklearn.cluster
• imblanced-learn
  • imblearn.under_sampling
  • imblearn.over_sampling
  • imblearn.combine
• skrebate
  • ReliefF
  • SURF
  • SURFstar
  • MultiSURF
  • MultiSURFstar

Custom implementations for biomedical application

• IRAPSClassifier
• BinarizeTargetClassifier/BinarizeTargetRegressor
• TDMScaler
• DyRFE/DyRFECV
• Z_RandomOverSampler

Examples

1. Build a simple randomforest model.