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Getting started

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Welcome to ramp’s documentation!

Ramp is a python package for rapid machine learning prototyping. It provides a simple, declarative syntax for exploring features, algorithms and transformations quickly and efficiently. At its core it’s a unified pandas-based framework for working with existing python machine learning and statistics libraries (scikit-learn, rpy2, etc.).


  • Fast caching and persistence of all intermediate and final calculations – nothing is recomputed unnecessarily.
  • Advanced training and preparation logic. Ramp respects the current training set, even when using complex trained features and blended predictions, and also tracks the given preparation set (the x values used in feature preparation – e.g. the mean and stdev used for feature normalization.)
  • A growing library of feature transformations, metrics and estimators. Ramp’s simple API allows for easy extension.


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