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<section id="related-projects">
<span id="id1"></span><h1>Related Projects<a class="headerlink" href="#related-projects" title="Link to this heading">#</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 organization</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="Link to this heading">#</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>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/alteryx/evalml">EvalML</a>
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>
<li><p><a class="reference external" href="https://github.com/mljar/mljar-supervised">MLJAR AutoML</a>
A Python package for AutoML on Tabular Data with Feature Engineering,
Hyper-Parameters Tuning, Explanations and Automatic Documentation.</p></li>
</ul>
<p><strong>Experimentation and model registry frameworks</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://mlflow.org/">MLFlow</a> An open source platform to manage the ML
lifecycle, including experimentation, reproducibility, deployment, and a central
model registry.</p></li>
<li><p><a class="reference external" href="https://neptune.ai/">Neptune</a> 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> A tool to help you configure,
organize, log and reproduce experiments</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 visualization</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://lorentzenchr.github.io/model-diagnostics/">model-diagnostics</a> Tools for
diagnostics and assessment of (machine learning) models (in Python).</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 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://skops.readthedocs.io/en/stable/persistence.html">skops.io</a> A
persistence model more secure than pickle, which can be used instead of
pickle in most common cases.</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://treelite.readthedocs.io">treelite</a>
Compiles tree-based ensemble models into C code for minimizing prediction
latency.</p></li>
<li><p><a class="reference external" href="https://emlearn.org">emlearn</a>
Implements scikit-learn estimators in C99 for embedded devices and microcontrollers.
Supports several classifier, regression and outlier detection models.</p></li>
</ul>
<p><strong>Model throughput</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex">Intel(R) Extension for scikit-learn</a>
Mostly on high end Intel(R) hardware, accelerates some scikit-learn models
for both training and inference under certain circumstances. This project is
maintained by Intel(R) and scikit-learn’s maintainers are not involved in the
development of this project. Also note that in some cases using the tools and
estimators under <code class="docutils literal notranslate"><span class="pre">scikit-learn-intelex</span></code> would give different results than
<code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> itself. If you encounter issues while using this project,
make sure you report potential issues in their respective repositories.</p></li>
</ul>
<p><strong>Interface to R with genomic applications</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://bioconductor.org/packages/BiocSklearn">BiocSklearn</a>
Exposes a small number of dimension reduction facilities as an illustration
of the basilisk protocol for interfacing Python with R. Intended as a
springboard for more complete interop.</p></li>
</ul>
</section>
<section id="other-estimators-and-tasks">
<h2>Other estimators and tasks<a class="headerlink" href="#other-estimators-and-tasks" title="Link to this heading">#</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>Time series and forecasting</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/aeon-toolkit/aeon">aeon</a> A
scikit-learn compatible toolbox for machine learning with time series
(fork of <a class="reference external" href="https://github.com/sktime/sktime">sktime</a>).</p></li>
<li><p><a class="reference external" href="https://unit8co.github.io/darts/">Darts</a> A Python library for
user-friendly forecasting and anomaly detection on time series. It contains a variety
of models, from classics such as ARIMA to deep neural networks. The forecasting
models can all be used in the same way, using fit() and predict() functions, similar
to scikit-learn.</p></li>
<li><p><a class="reference external" href="https://github.com/sktime/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/JoaquinAmatRodrigo/skforecast">skforecast</a> A Python library
that eases using scikit-learn regressors as multi-step forecasters. It also works
with any regressor compatible with the scikit-learn API.</p></li>
<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>
</ul>
<p><strong>Gradient (tree) boosting</strong></p>
<p>Note scikit-learn own modern gradient boosting estimators
<a class="reference internal" href="modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradientBoostingClassifier" title="sklearn.ensemble.HistGradientBoostingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">HistGradientBoostingClassifier</span></code></a> and
<a class="reference internal" href="modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble.HistGradientBoostingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">HistGradientBoostingRegressor</span></code></a>.</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/dmlc/xgboost">XGBoost</a> XGBoost is an optimized distributed
gradient boosting library designed to be highly efficient, flexible and portable.</p></li>
<li><p><a class="reference external" href="https://lightgbm.readthedocs.io">LightGBM</a> LightGBM is a gradient boosting
framework that uses tree based learning algorithms. It is designed to be distributed
and efficient.</p></li>
</ul>
<p><strong>Structured learning</strong></p>
<ul class="simple">
<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://github.com/jmschrei/pomegranate">pomegranate</a> Probabilistic modelling
for Python, with an emphasis on hidden Markov models.</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/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>Federated Learning</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://flower.dev/">Flower</a> A friendly federated learning framework with a
unified approach that can federate any workload, any ML framework, and any programming language.</p></li>
</ul>
<p><strong>Privacy Preserving Machine Learning</strong></p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/zama-ai/concrete-ml/">Concrete ML</a> A privacy preserving
ML framework built on top of <a class="reference external" href="https://github.com/zama-ai/concrete">Concrete</a>, with bindings to traditional ML
frameworks, thanks to fully homomorphic encryption. APIs of so-called
Concrete ML built-in models are very close to scikit-learn APIs.</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/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>
</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.
As of scikit-learn version 1.3.0, there is <a class="reference internal" href="modules/generated/sklearn.cluster.HDBSCAN.html#sklearn.cluster.HDBSCAN" title="sklearn.cluster.HDBSCAN"><code class="xref py py-class docutils literal notranslate"><span class="pre">HDBSCAN</span></code></a>.</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.
As of scikit-learn version 1.3.0, there is
<a class="reference internal" href="modules/generated/sklearn.preprocessing.TargetEncoder.html#sklearn.preprocessing.TargetEncoder" title="sklearn.preprocessing.TargetEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">TargetEncoder</span></code></a>.</p></li>
<li><p><a class="reference external" href="https://skrub-data.org">skrub</a> : facilitate learning on dataframes,
with sklearn compatible encoders (of categories, dates, strings) and
more.</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="Link to this heading">#</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://www.pymc.io/">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> 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>
<section id="recommendation-engine-packages">
<h2>Recommendation Engine packages<a class="headerlink" href="#recommendation-engine-packages" title="Link to this heading">#</a></h2>
<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://surpriselib.com/">Surprise Lib</a> Library for explicit feedback
datasets.</p></li>
</ul>
</section>
<section id="domain-specific-packages">
<h2>Domain specific packages<a class="headerlink" href="#domain-specific-packages" title="Link to this heading">#</a></h2>
<ul class="simple">
<li><p><a class="reference external" href="https://scikit-network.readthedocs.io/">scikit-network</a> Machine learning on graphs.</p></li>
<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>
</ul>
</section>
<section id="translations-of-scikit-learn-documentation">
<h2>Translations of scikit-learn documentation<a class="headerlink" href="#translations-of-scikit-learn-documentation" title="Link to this heading">#</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="id2" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></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>
<li><p><a class="reference external" href="https://panda5176.github.io/scikit-learn-korean/">Korean translation</a>
(<a class="reference external" href="https://github.com/panda5176/scikit-learn-korean">source</a>)</p></li>
</ul>
<p class="rubric">Footnotes</p>
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