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Welcome to PipeGraph!
============================================

Introduction
------------

Scikit-Learn's Pipeline is a very useful tool to bind a sequence of transformers and a final estimator
in a single unit capable of working itself as an estimator. This eases managing the fit and predict
of the complete sequence of steps through a simple call to their fit and predict methods,
and in combination with the GridSearchCV function allows to tune the hyperparameters of these steps
while in the search for the best model.

PipeGraph extends the concept of Pipeline by using a graph structure that can handle Scikit-Learn's
objects in imaginative layouts. This allows the user to:

- Express simple and complex sequences of steps, from linear workflows to elaborated graphs
- To have access to all the data generated along the intermediate steps
- To use GridSearchCV on those graphs

Let's see a few examples to understand how we can express these situations through PipeGraph objects.


    .. toctree::
       :maxdepth: 2

       api
       User_Guide
       auto_examples/index



Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`