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<span id="id1"></span><h1>Related Projects<a class="headerlink" href="#related-projects" title="Permalink to this headline">¶</a></h1>
<p>Below is a list of sister-projects, extensions and domain specific packages.</p>
<div class="section" id="related-packages">
<h2>Related Packages<a class="headerlink" href="#related-packages" title="Permalink to this headline">¶</a></h2>
<p>Other packages useful for data analysis and machine learning.</p>
<ul class="simple">
<li><a class="reference external" href="http://pandas.pydata.org">Pandas</a> Tools for working with heterogeneous and
columnar data, relational queries, time series and basic statistics.</li>
<li><a class="reference external" href="https://github.com/paulgb/sklearn-pandas/">sklearn_pandas</a> bridge for
scikit-learn pipelines and pandas data frame with dedicated transformers.</li>
<li><a class="reference external" href="https://skll.readthedocs.org/en/latest/index.html">Scikit-Learn Laboratory</a> A command-line
wrapper around scikit-learn that makes it easy to run machine learning
experiments with multiple learners and large feature sets.</li>
<li><a class="reference external" href="http://deeplearning.net/software/theano/">theano</a> A CPU/GPU array
processing framework geared towards deep learning research.</li>
<li><a class="reference external" href="http://statsmodels.sourceforge.net/">Statsmodel</a> Estimating and analysing
statistical models. More focused on statistical tests and less on prediction
than scikit-learn.</li>
<li><a class="reference external" href="http://pymc-devs.github.io/pymc/">PyMC</a> Bayesian statistical models and fitting algorithms.</li>
</ul>
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<div class="section" id="extensions-and-algorithms">
<h2>Extensions and Algorithms<a class="headerlink" href="#extensions-and-algorithms" title="Permalink to this headline">¶</a></h2>
<p>Libraries that provide a scikit-learn like interface and can be used with
scikit-learn tools.</p>
<ul class="simple">
<li><a class="reference external" href="http://deeplearning.net/software/pylearn2/">pylearn2</a> A deep learning and
neural network library build on theano with scikit-learn like interface.</li>
<li><a class="reference external" href="http://www.mblondel.org/lightning/">lightning</a> Fast state-of-the-art
linear model solvers (SDCA, AdaGrad, SVRG, SAG, etc...).</li>
<li><a class="reference external" href="https://github.com/larsmans/seqlearn">Seqlearn</a> Sequence classification
using HMMs or structured perceptron.</li>
<li><a class="reference external" href="https://github.com/hmmlearn/hmmlearn">HMMLearn</a> Implementation of hidden
markov models that was previously part of scikit-learn.</li>
<li><a class="reference external" href="https://pystruct.githup.io">PyStruct</a> General conditional random fields
and structured prediction.</li>
<li><a class="reference external" href="https://github.com/jcrudy/py-earth">py-earth</a> Multivariate adaptive regression splines</li>
<li><a class="reference external" href="https://github.com/ajtulloch/sklearn-compiledtrees/">sklearn-compiledtrees</a>
Generate a C++ implementation of the predict function for decision trees (and
ensembles) trained by sklearn. Useful for latency-sensitive production
environments.</li>
<li><a class="reference external" href="https://github.com/ariddell/lda/">lda</a>: Fast implementation of Latent
Dirichlet Allocation in Cython.</li>
<li><a class="reference external" href="https://github.com/jmetzen/sparse-filtering">Sparse Filtering</a>
Unsupervised feature learning based on sparse-filtering</li>
<li><a class="reference external" href="https://github.com/jmetzen/kernel_regression">Kernel Regression</a>
Implementation of Nadaraya-Watson kernel regression with automatic bandwidth
selection</li>
</ul>
</div>
<div class="section" id="domain-specific-packages">
<h2>Domain Specific Packages<a class="headerlink" href="#domain-specific-packages" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><a class="reference external" href="http://scikit-image.org/">scikit-image</a> Image processing and computer vision in python.</li>
<li><a class="reference external" href="http://www.nltk.org/">Natural language toolkit (nltk)</a> Natual language processing and some machine learning.</li>
<li><a class="reference external" href="https://nilearn.github.io/">NiLearn</a> Machine learning for neuro-imaging.</li>
<li><a class="reference external" href="http://www.astroml.org/">AstroML</a> Machine learning for astronomy.</li>
</ul>
</div>
<div class="section" id="snippets-and-tidbits">
<h2>Snippets and tidbits<a class="headerlink" href="#snippets-and-tidbits" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/wiki/Third-party-projects-and-code-snippets">wiki</a> has more!</p>
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