<commit_before>========
Overview
========

Reskit (researcher’s kit) is a library for creating and curating reproducible pipelines for scientific and industrial machine learning. The natural extension of the ``scikit-learn`` Pipelines to general classes of pipelines, Reskit allows for the efficient and transparent optimization of each pipeline step. Main features include data caching, compatibility with most of the scikit-learn objects, optimization constraints (e.g. forbidden combinations), and table generation for quality metrics. Reskit also allows for the injection of custom metrics into the underlying scikit frameworks. Reskit is intended for use by researchers who need pipelines amenable to versioning and reproducibility, yet who also have a large volume of experiments to run.

.. contents::

Goals
-----

Reskit is intended to provide
  * makes possible simply reproducible experiments
  * graph's matrix norms and metrics

Getting started: A Short Introduction to Reskit
-----------------------------------------------

Let's say we have two datasets, and we want to compare some pipelines on
this datasets. Machine learning pipelines represent a list of transforms
and a final estimator. For instance, we want to try two different
classifiers and two different scale methods.

We define functions of loading datasets. <commit_msg>Update overview.rst<commit_after>========
Overview
========

Reskit (researcher’s kit) is a library for creating and curating reproducible pipelines for scientific and industrial machine learning. The natural extension of the ``scikit-learn`` Pipelines to general classes of pipelines, Reskit allows for the efficient and transparent optimization of each pipeline step. Main features include data caching, compatibility with most of the scikit-learn objects, optimization constraints (e.g. forbidden combinations), and table generation for quality metrics. Reskit also allows for the injection of custom metrics into the underlying scikit frameworks. Reskit is intended for use by researchers who need pipelines amenable to versioning and reproducibility, yet who also have a large volume of experiments to run.

.. contents::

Features
-----

Reskit is intended to provide
  * makes possible simply reproducible experiments
  * graph's matrix norms and metrics

Getting started: A Short Introduction to Reskit
-----------------------------------------------

Let's say we have two datasets, and we want to compare some pipelines on
this datasets. Machine learning pipelines represent a list of transforms
and a final estimator. For instance, we want to try two different
classifiers and two different scale methods.

We define functions of loading datasets. 
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