Welcome to REP’s documentation!

REP (Reproducible Experiment Platform) library provides functionality for all your basic needs to deal with machine learning.

It includes:

• Data provides operations with data

• Estimators (classification and regression) is sklearn-like wrappers for variety of machine learning libraries:

  • TMVA
  • Sklearn
  • XGBoost
  • Pybrain
  • Neurolab
  • Theanets.

  These can be used as base estimators in sklearn.

• Meta Machine Learning contains factory (the set of estimators), grid search, folding algorithm. Also parallel execution on a cluster is supported

• Report for models contains helpful classes to get model result information on any dataset

• Plotting is wrapper for different plotting libraries including interactive plots

  • matplotlib
  • bokeh
  • tmva
  • plotly

• Utilities contains additional functions

• Howto notebooks contains examples

Main repository: http://github.com/yandex/rep

Contents:

• Data
  • Storage
• Estimators (classification and regression)
  • Estimators interfaces (for classification and regression)
  • Sklearn classifier and regressor
  • TMVA classifier and regressor
  • XGBoost classifier and regressor
  • Theanets classifier and regressor
  • Neurolab classifier and regressor
  • Pybrain classifier and regressor
  • Examples
• Meta Machine Learning
  • Factory
  • Factory Examples
  • Grid Search
  • Folding
  • Stacking
• Report for models
  • Classification Report
  • Regression Report
  • Metrics
• Plotting
• Utilities
• Howto notebooks