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<li><a class="reference internal" href="#interoperability-and-framework-enhancements">Interoperability and framework enhancements</a></li>
<li><a class="reference internal" href="#other-estimators-and-tasks">Other estimators and tasks</a></li>
<li><a class="reference internal" href="#statistical-learning-with-python">Statistical learning with Python</a><ul>
<li><a class="reference internal" href="#recommendation-engine-packages">Recommendation Engine packages</a></li>
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<section id="related-projects">
<span id="id1"></span><h1>Related Projects<a class="headerlink" href="#related-projects" title="Permalink to this headline">¶</a></h1>
<p>Projects implementing the scikit-learn estimator API are encouraged to use
the <a class="reference external" href="https://github.com/scikit-learn-contrib/project-template">scikit-learn-contrib template</a>
which facilitates best practices for testing and documenting estimators.
The <a class="reference external" href="https://github.com/scikit-learn-contrib/scikit-learn-contrib">scikit-learn-contrib GitHub organisation</a>
also accepts high-quality contributions of repositories conforming to this
template.</p>
<p>Below is a list of sister-projects, extensions and domain specific packages.</p>
<section id="interoperability-and-framework-enhancements">
<h2>Interoperability and framework enhancements<a class="headerlink" href="#interoperability-and-framework-enhancements" title="Permalink to this headline">¶</a></h2>
<p>These tools adapt scikit-learn for use with other technologies or otherwise
enhance the functionality of scikit-learn’s estimators.</p>
<p><strong>Data formats</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/mblondel/svmlight-loader">Fast svmlight / libsvm file loader</a>
Fast and memory-efficient svmlight / libsvm file loader for Python.</p></li>
<li><p><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.</p></li>
<li><p><a class="reference external" href="https://github.com/phausamann/sklearn-xarray/">sklearn_xarray</a> provides
compatibility of scikit-learn estimators with xarray data structures.</p></li>
</ul>
<p><strong>Auto-ML</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/automl/auto-sklearn/">auto-sklearn</a>
An automated machine learning toolkit and a drop-in replacement for a
scikit-learn estimator</p></li>
<li><p><a class="reference external" href="https://github.com/AutoViML/Auto_ViML/">autoviml</a>
Automatically Build Multiple Machine Learning Models with a Single Line of Code.
Designed as a faster way to use scikit-learn models without having to preprocess data.</p></li>
<li><p><a class="reference external" href="https://github.com/rhiever/tpot">TPOT</a>
An automated machine learning toolkit that optimizes a series of scikit-learn
operators to design a machine learning pipeline, including data and feature
preprocessors as well as the estimators. Works as a drop-in replacement for a
scikit-learn estimator.</p></li>
<li><p><a class="reference external" href="https://github.com/alteryx/featuretools">Featuretools</a>
A framework to perform automated feature engineering. It can be used for
transforming temporal and relational datasets into feature matrices for
machine learning.</p></li>
<li><p><a class="reference external" href="https://github.com/Neuraxio/Neuraxle">Neuraxle</a>
A library for building neat pipelines, providing the right abstractions to
both ease research, development, and deployment of machine learning
applications. Compatible with deep learning frameworks and scikit-learn API,
it can stream minibatches, use data checkpoints, build funky pipelines, and
serialize models with custom per-step savers.</p></li>
<li><p><a class="reference external" href="https://github.com/alteryx/evalml">EvalML</a>
EvalML is an AutoML library which builds, optimizes, and evaluates
machine learning pipelines using domain-specific objective functions.
It incorporates multiple modeling libraries under one API, and
the objects that EvalML creates use an sklearn-compatible API.</p></li>
</ul>
<p><strong>Experimentation frameworks</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://neptune.ai/">Neptune</a> Metadata store for MLOps,
built for teams that run a lot of experiments. It gives you a single
place to log, store, display, organize, compare, and query all your
model building metadata.</p></li>
<li><p><a class="reference external" href="https://github.com/IDSIA/Sacred">Sacred</a> Tool to help you configure,
organize, log and reproduce experiments</p></li>
<li><p><a class="reference external" href="https://github.com/yandex/REP">REP</a> Environment for conducting data-driven
research in a consistent and reproducible way</p></li>
<li><p><a class="reference external" href="https://skll.readthedocs.io/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.</p></li>
</ul>
<p><strong>Model inspection and visualisation</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/parrt/dtreeviz/">dtreeviz</a> A python library for
decision tree visualization and model interpretation.</p></li>
<li><p><a class="reference external" href="https://github.com/TeamHG-Memex/eli5/">eli5</a> A library for
debugging/inspecting machine learning models and explaining their
predictions.</p></li>
<li><p><a class="reference external" href="https://github.com/rasbt/mlxtend">mlxtend</a> Includes model visualization
utilities.</p></li>
<li><p><a class="reference external" href="https://github.com/DistrictDataLabs/yellowbrick">yellowbrick</a> A suite of
custom matplotlib visualizers for scikit-learn estimators to support visual feature
analysis, model selection, evaluation, and diagnostics.</p></li>
</ul>
<p><strong>Model selection</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://scikit-optimize.github.io/">scikit-optimize</a>
A library to minimize (very) expensive and noisy black-box functions. It
implements several methods for sequential model-based optimization, and
includes a replacement for <code class="docutils literal notranslate"><span class="pre">GridSearchCV</span></code> or <code class="docutils literal notranslate"><span class="pre">RandomizedSearchCV</span></code> to do
cross-validated parameter search using any of these strategies.</p></li>
<li><p><a class="reference external" href="https://github.com/rsteca/sklearn-deap">sklearn-deap</a> Use evolutionary
algorithms instead of gridsearch in scikit-learn.</p></li>
</ul>
<p><strong>Model export for production</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/onnx/sklearn-onnx">sklearn-onnx</a> Serialization of many
Scikit-learn pipelines to <a class="reference external" href="https://onnx.ai/">ONNX</a> for interchange and
prediction.</p></li>
<li><p><a class="reference external" href="https://github.com/jpmml/sklearn2pmml">sklearn2pmml</a>
Serialization of a wide variety of scikit-learn estimators and transformers
into PMML with the help of <a class="reference external" href="https://github.com/jpmml/jpmml-sklearn">JPMML-SkLearn</a>
library.</p></li>
<li><p><a class="reference external" href="https://github.com/nok/sklearn-porter">sklearn-porter</a>
Transpile trained scikit-learn models to C, Java, Javascript and others.</p></li>
<li><p><a class="reference external" href="https://github.com/BayesWitnesses/m2cgen">m2cgen</a>
A lightweight library which allows to transpile trained machine learning
models including many scikit-learn estimators into a native code of C, Java,
Go, R, PHP, Dart, Haskell, Rust and many other programming languages.</p></li>
<li><p><a class="reference external" href="https://treelite.readthedocs.io">treelite</a>
Compiles tree-based ensemble models into C code for minimizing prediction
latency.</p></li>
</ul>
</section>
<section id="other-estimators-and-tasks">
<h2>Other estimators and tasks<a class="headerlink" href="#other-estimators-and-tasks" title="Permalink to this headline">¶</a></h2>
<p>Not everything belongs or is mature enough for the central scikit-learn
project. The following are projects providing interfaces similar to
scikit-learn for additional learning algorithms, infrastructures
and tasks.</p>
<p><strong>Structured learning</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/tslearn-team/tslearn">tslearn</a> A machine learning library for time series
that offers tools for pre-processing and feature extraction as well as dedicated models for clustering, classification and regression.</p></li>
<li><p><a class="reference external" href="https://github.com/alan-turing-institute/sktime">sktime</a> A scikit-learn compatible toolbox for machine learning with time series including time series classification/regression and (supervised/panel) forecasting.</p></li>
<li><p><a class="reference external" href="https://github.com/hmmlearn/hmmlearn">HMMLearn</a> Implementation of hidden
markov models that was previously part of scikit-learn.</p></li>
<li><p><a class="reference external" href="https://pystruct.github.io">PyStruct</a> General conditional random fields
and structured prediction.</p></li>
<li><p><a class="reference external" href="https://github.com/jmschrei/pomegranate">pomegranate</a> Probabilistic modelling
for Python, with an emphasis on hidden Markov models.</p></li>
<li><p><a class="reference external" href="https://github.com/TeamHG-Memex/sklearn-crfsuite">sklearn-crfsuite</a>
Linear-chain conditional random fields
(<a class="reference external" href="http://www.chokkan.org/software/crfsuite/">CRFsuite</a> wrapper with
sklearn-like API).</p></li>
</ul>
<p><strong>Deep neural networks etc.</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/dnouri/nolearn">nolearn</a> A number of wrappers and
abstractions around existing neural network libraries</p></li>
<li><p><a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras">Keras</a> High-level API for
TensorFlow with a scikit-learn inspired API.</p></li>
<li><p><a class="reference external" href="https://github.com/Lasagne/Lasagne">lasagne</a> A lightweight library to
build and train neural networks in Theano.</p></li>
<li><p><a class="reference external" href="https://github.com/dnouri/skorch">skorch</a> A scikit-learn compatible
neural network library that wraps PyTorch.</p></li>
<li><p><a class="reference external" href="https://github.com/adriangb/scikeras">scikeras</a> provides a wrapper around
Keras to interface it with scikit-learn. SciKeras is the successor
of <code class="docutils literal notranslate"><span class="pre">tf.keras.wrappers.scikit_learn</span></code>.</p></li>
</ul>
<p><strong>Broad scope</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/rasbt/mlxtend">mlxtend</a> Includes a number of additional
estimators as well as model visualization utilities.</p></li>
<li><p><a class="reference external" href="https://github.com/koaning/scikit-lego">scikit-lego</a> A number of scikit-learn compatible
custom transformers, models and metrics, focusing on solving practical industry tasks.</p></li>
</ul>
<p><strong>Other regression and classification</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/dmlc/xgboost">xgboost</a> Optimised gradient boosted decision
tree library.</p></li>
<li><p><a class="reference external" href="https://mlens.readthedocs.io/">ML-Ensemble</a> Generalized
ensemble learning (stacking, blending, subsemble, deep ensembles,
etc.).</p></li>
<li><p><a class="reference external" href="https://github.com/scikit-learn-contrib/lightning">lightning</a> Fast
state-of-the-art linear model solvers (SDCA, AdaGrad, SVRG, SAG, etc…).</p></li>
<li><p><a class="reference external" href="https://github.com/scikit-learn-contrib/py-earth">py-earth</a> Multivariate
adaptive regression splines</p></li>
<li><p><a class="reference external" href="https://github.com/jmetzen/kernel_regression">Kernel Regression</a>
Implementation of Nadaraya-Watson kernel regression with automatic bandwidth
selection</p></li>
<li><p><a class="reference external" href="https://github.com/trevorstephens/gplearn">gplearn</a> Genetic Programming
for symbolic regression tasks.</p></li>
<li><p><a class="reference external" href="https://github.com/scikit-multilearn/scikit-multilearn">scikit-multilearn</a>
Multi-label classification with focus on label space manipulation.</p></li>
<li><p><a class="reference external" href="https://github.com/dmbee/seglearn">seglearn</a> Time series and sequence
learning using sliding window segmentation.</p></li>
<li><p><a class="reference external" href="https://github.com/jppbsi/LibOPF">libOPF</a> Optimal path forest classifier</p></li>
<li><p><a class="reference external" href="https://github.com/ibayer/fastFM">fastFM</a> Fast factorization machine
implementation compatible with scikit-learn</p></li>
</ul>
<p><strong>Decomposition and clustering</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/lda-project/lda/">lda</a>: Fast implementation of latent
Dirichlet allocation in Cython which uses <a class="reference external" href="https://en.wikipedia.org/wiki/Gibbs_sampling">Gibbs sampling</a> to sample from the true
posterior distribution. (scikit-learn’s
<a class="reference internal" href="modules/generated/sklearn.decomposition.LatentDirichletAllocation.html#sklearn.decomposition.LatentDirichletAllocation" title="sklearn.decomposition.LatentDirichletAllocation"><code class="xref py py-class docutils literal notranslate"><span class="pre">LatentDirichletAllocation</span></code></a> implementation uses
<a class="reference external" href="https://en.wikipedia.org/wiki/Variational_Bayesian_methods">variational inference</a> to sample from
a tractable approximation of a topic model’s posterior distribution.)</p></li>
<li><p><a class="reference external" href="https://github.com/nicodv/kmodes">kmodes</a> k-modes clustering algorithm for
categorical data, and several of its variations.</p></li>
<li><p><a class="reference external" href="https://github.com/scikit-learn-contrib/hdbscan">hdbscan</a> HDBSCAN and Robust Single
Linkage clustering algorithms for robust variable density clustering.</p></li>
<li><p><a class="reference external" href="https://github.com/clara-labs/spherecluster">spherecluster</a> Spherical
K-means and mixture of von Mises Fisher clustering routines for data on the
unit hypersphere.</p></li>
</ul>
<p><strong>Pre-processing</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/scikit-learn-contrib/categorical-encoding">categorical-encoding</a> A
library of sklearn compatible categorical variable encoders.</p></li>
<li><p><a class="reference external" href="https://github.com/scikit-learn-contrib/imbalanced-learn">imbalanced-learn</a> Various
methods to under- and over-sample datasets.</p></li>
<li><p><a class="reference external" href="https://github.com/solegalli/feature_engine">Feature-engine</a> A library
of sklearn compatible transformers for missing data imputation, categorical
encoding, variable transformation, discretization, outlier handling and more.
Feature-engine allows the application of preprocessing steps to selected groups
of variables and it is fully compatible with the Scikit-learn Pipeline.</p></li>
</ul>
<p><strong>Topological Data Analysis</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/giotto-ai/giotto-tda">giotto-tda</a> A library for
<a class="reference external" href="https://en.wikipedia.org/wiki/Topological_data_analysis">Topological Data Analysis</a> aiming to
provide a scikit-learn compatible API. It offers tools to transform data
inputs (point clouds, graphs, time series, images) into forms suitable for
computations of topological summaries, and components dedicated to
extracting sets of scalar features of topological origin, which can be used
alongside other feature extraction methods in scikit-learn.</p></li>
</ul>
</section>
<section id="statistical-learning-with-python">
<h2>Statistical learning with Python<a class="headerlink" href="#statistical-learning-with-python" title="Permalink to this headline">¶</a></h2>
<p>Other packages useful for data analysis and machine learning.</p>
<ul class="simple">
<li><p><a class="reference external" href="https://pandas.pydata.org/">Pandas</a> Tools for working with heterogeneous and
columnar data, relational queries, time series and basic statistics.</p></li>
<li><p><a class="reference external" href="https://www.statsmodels.org">statsmodels</a> Estimating and analysing
statistical models. More focused on statistical tests and less on prediction
than scikit-learn.</p></li>
<li><p><a class="reference external" href="https://pymc-devs.github.io/pymc/">PyMC</a> Bayesian statistical models and
fitting algorithms.</p></li>
<li><p><a class="reference external" href="https://stanford.edu/~mwaskom/software/seaborn/">Seaborn</a> Visualization library based on
matplotlib. It provides a high-level interface for drawing attractive statistical graphics.</p></li>
<li><p><a class="reference external" href="https://scikit-survival.readthedocs.io/">scikit-survival</a> A library implementing
models to learn from censored time-to-event data (also called survival analysis).
Models are fully compatible with scikit-learn.</p></li>
</ul>
<section id="recommendation-engine-packages">
<h3>Recommendation Engine packages<a class="headerlink" href="#recommendation-engine-packages" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/benfred/implicit">implicit</a>, Library for implicit
feedback datasets.</p></li>
<li><p><a class="reference external" href="https://github.com/lyst/lightfm">lightfm</a> A Python/Cython
implementation of a hybrid recommender system.</p></li>
<li><p><a class="reference external" href="https://github.com/ylongqi/openrec">OpenRec</a> TensorFlow-based
neural-network inspired recommendation algorithms.</p></li>
<li><p><a class="reference external" href="https://github.com/maciejkula/spotlight">Spotlight</a> Pytorch-based
implementation of deep recommender models.</p></li>
<li><p><a class="reference external" href="http://surpriselib.com/">Surprise Lib</a> Library for explicit feedback
datasets.</p></li>
</ul>
</section>
<section id="domain-specific-packages">
<h3>Domain specific packages<a class="headerlink" href="#domain-specific-packages" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><p><a class="reference external" href="https://scikit-image.org/">scikit-image</a> Image processing and computer
vision in python.</p></li>
<li><p><a class="reference external" href="https://www.nltk.org/">Natural language toolkit (nltk)</a> Natural language
processing and some machine learning.</p></li>
<li><p><a class="reference external" href="https://radimrehurek.com/gensim/">gensim</a> A library for topic modelling,
document indexing and similarity retrieval</p></li>
<li><p><a class="reference external" href="https://nilearn.github.io/">NiLearn</a> Machine learning for neuro-imaging.</p></li>
<li><p><a class="reference external" href="https://www.astroml.org/">AstroML</a> Machine learning for astronomy.</p></li>
<li><p><a class="reference external" href="http://msmbuilder.org/">MSMBuilder</a> Machine learning for protein
conformational dynamics time series.</p></li>
</ul>
</section>
</section>
<section id="translations-of-scikit-learn-documentation">
<h2>Translations of scikit-learn documentation<a class="headerlink" href="#translations-of-scikit-learn-documentation" title="Permalink to this headline">¶</a></h2>
<p>Translation’s purpose is to ease reading and understanding in languages
other than English. Its aim is to help people who do not understand English
or have doubts about its interpretation. Additionally, some people prefer
to read documentation in their native language, but please bear in mind that
the only official documentation is the English one <a class="footnote-reference brackets" href="#f1" id="id3">1</a>.</p>
<p>Those translation efforts are community initiatives and we have no control
on them.
If you want to contribute or report an issue with the translation, please
contact the authors of the translation.
Some available translations are linked here to improve their dissemination
and promote community efforts.</p>
<ul class="simple">
<li><p><a class="reference external" href="https://sklearn.apachecn.org/">Chinese translation</a>
(<a class="reference external" href="https://github.com/apachecn/sklearn-doc-zh">source</a>)</p></li>
<li><p><a class="reference external" href="https://sklearn.ir/">Persian translation</a>
(<a class="reference external" href="https://github.com/mehrdad-dev/scikit-learn">source</a>)</p></li>
<li><p><a class="reference external" href="https://qu4nt.github.io/sklearn-doc-es/">Spanish translation</a>
(<a class="reference external" href="https://github.com/qu4nt/sklearn-doc-es">source</a>)</p></li>
</ul>
<p class="rubric">Footnotes</p>
<dl class="footnote brackets">
<dt class="label" id="f1"><span class="brackets"><a class="fn-backref" href="#id3">1</a></span></dt>
<dd><p>following <a class="reference external" href="https://www.kernel.org/doc/html/latest/translations/index.html#disclaimer">linux documentation Disclaimer</a></p>
</dd>
</dl>
</section>
</section>
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