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index files of documentation udpated
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Mohammed Fazil committed Aug 2, 2023
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12 changes: 6 additions & 6 deletions docs/_modules/index.html
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<div itemprop="articleBody">

<h1>All modules for which code is available</h1>
<ul><li><a href="exponential_smoothing.html">exponential_smoothing</a></li>
<li><a href="lgb_to_pmml.html">lgb_to_pmml</a></li>
<li><a href="pre_process.html">pre_process</a></li>
<li><a href="skl_to_pmml.html">skl_to_pmml</a></li>
<li><a href="statsmodels_to_pmml.html">statsmodels_to_pmml</a></li>
<li><a href="xgboost_to_pmml.html">xgboost_to_pmml</a></li>
<ul><li><a href="exponential_smoothing.html">ExponentialSmoothing Exporter</a></li>
<li><a href="lgb_to_pmml.html">LightGBM Exporter</a></li>
<li><a href="pre_process.html">Pre-Processing Exporter</a></li>
<li><a href="skl_to_pmml.html">Scikit-Learn Exporter</a></li>
<li><a href="statsmodels_to_pmml.html">Statsmodels Exporter</a></li>
<li><a href="xgboost_to_pmml.html">XGBoost Exporter</a></li>
</ul>

</div>
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<!DOCTYPE html>
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<title>Nyoka 5.5.0 &mdash; A python library for PMML generation</title>








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<nav data-toggle="wy-nav-shift" class="wy-nav-side">
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<a href="#" class="icon icon-home"> Nyoka</a>




<div class="version">
5.5.0
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<a href="#" class="icon icon-home">
Nyoka
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<ul>
<li class="toctree-l1"><a class="reference internal" href="exponential_smoothing.html">ExponentialSmoothing Exporter Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="lgb_to_pmml.html">LightGBM Exporter Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="pre_process.html">Pre-Processing Exporter Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="skl_to_pmml.html">Scikit-Learn Exporter Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="statsmodels_to_pmml.html">Statsmodels Exporter Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="xgboost_to_pmml.html">XGBoost Exporter Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="exponential_smoothing.html">ExponentialSmoothing Exporter Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="lgb_to_pmml.html">LightGBM Exporter Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="pre_process.html">Pre-Processing Exporter Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="skl_to_pmml.html">Scikit-Learn Exporter Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="statsmodels_to_pmml.html">Statsmodels Exporter Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="xgboost_to_pmml.html">XGBoost Exporter Module</a></li>
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<div class="section" id="scripts">
<div class="section" id="welcome-to-nyoka-s-documentation">
<h1>Welcome to Nyoka’s documentation!<a class="headerlink" href="#welcome-to-nyoka-s-documentation" title="Permalink to this headline"></a></h1>
<img src="https://raw.githubusercontent.com/SoftwareAG/nyoka/master/docs/nyoka_logo.PNG" width="250px" aria-placeholder="Nyoka logo"/>
<p>Nyoka is a Python library for comprehensive support of the latest PMML (PMML 4.4.1) standard. Using Nyoka, Data Scientists can export a large number of Machine Learning models from popular Python frameworks into PMML by either using any of the numerous included ready-to-use exporters or by creating their own exporter for specialized/individual model types by simply calling a sequence of constructors.</p>
<p>Besides about 500 Python classes which each cover a PMML tag and all constructor parameters/attributes as defined in the standard, Nyoka also provides an increasing number of convenience classes and functions that make the Data Scientist’s life easier for example by reading or writing any PMML file in one line of code from within your favorite Python environment.</p>
<p>Nyoka comes to you with the complete source code in Python, an extended HTML documentation for the classes/functions and a growing number of Jupyter Notebook tutorials that help you familiarizing yourself with the way how Nyoka supports you to use PMML as your favorite Data Science transport file format.</p>

<div class="toctree-wrapper compound">
<h3>The core modules of Nyoka are</h3>
<ul>
<li class="toctree-l1"><a class="reference internal" href="exponential_smoothing.html">ExponentialSmoothing Exporter Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="lgb_to_pmml.html">LightGBM Exporter Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="pre_process.html">Pre-Processing Exporter Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="skl_to_pmml.html">Scikit-Learn Exporter Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="statsmodels_to_pmml.html">Statsmodels Exporter Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="xgboost_to_pmml.html">XGBoost Exporter Module</a></li>
</ul>
</div>

<section id="scripts">
<div class="section" id="welcome-to-nyoka-s-documentation">
<h1>Welcome to Nyoka’s documentation!<a class="headerlink" href="#welcome-to-nyoka-s-documentation"
title="Permalink to this headline"></a></h1>
<img src="https://raw.githubusercontent.com/SoftwareAG/nyoka/master/docs/nyoka_logo.PNG" width="250px"
aria-placeholder="Nyoka logo" />
<p>Nyoka is a Python library for comprehensive support of the latest PMML (PMML 4.4.1) standard. Using
Nyoka, Data Scientists can export a large number of Machine Learning models from popular Python
frameworks into PMML by either using any of the numerous included ready-to-use exporters or by
creating their own exporter for specialized/individual model types by simply calling a sequence of
constructors.</p>
<p>Besides about 500 Python classes which each cover a PMML tag and all constructor
parameters/attributes as defined in the standard, Nyoka also provides an increasing number of
convenience classes and functions that make the Data Scientist’s life easier for example by reading
or writing any PMML file in one line of code from within your favorite Python environment.</p>
<p>Nyoka comes to you with the complete source code in Python, an extended HTML documentation for the
classes/functions and a growing number of Jupyter Notebook tutorials that help you familiarizing
yourself with the way how Nyoka supports you to use PMML as your favorite Data Science transport
file format.</p>

<div class="toctree-wrapper compound">
<h3>The core modules of Nyoka are</h3>
<ul>
<li class="toctree-l1"><a class="reference internal" href="statsmodels_to_pmml.html">Statsmodels
Exporter Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="lgb_to_pmml.html">LightGBM Exporter
Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="pre_process.html">Pre-Processing
Exporter Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="skl_to_pmml.html">Scikit-Learn Exporter
Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="xgboost_to_pmml.html">XGBoost Exporter
Module</a></li>
<li class="toctree-l1"><a class="reference internal"
href="exponential_smoothing.html">ExponentialSmoothing Exporter Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="preprocess_nyoka.html">Nyoka's
Pre-Processing Module</a></li>
</ul>
</div>


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