Journal of Machine Learning Research 12 (2011) 2825-2830

Submitted 3/11; Revised 8/11; Published 10/11

Scikit-learn: Machine Learning in Python
Fabian Pedregosa
Gaël VaroquauxAlexandre Gramfort
Vincent Michel
Bertrand Thirion

FABIAN . PEDREGOSA @ INRIA . FR
GAEL . VAROQUAUX @ NORMALESUP. ORG
ALEXANDRE . GRAMFORT @ INRIA . FR
VINCENT. MICHEL @ LOGILAB . FR
BERTRAND . THIRION @ INRIA . FR

Parietal, INRIA Saclay
Neurospin, Bât 145, CEA Saclay
91191 Gif sur Yvette – France

Olivier Grisel

OLIVIER . GRISEL @ ENSTA . FR

Nuxeo
20 rue Soleillet
75 020 Paris – FranceMathieu Blondel

MBLONDEL @ AI . CS . KOBE - U . AC . JP

Kobe University
1-1 Rokkodai, Nada
Kobe 657-8501 – Japan

Peter Prettenhofer

PETER . PRETTENHOFER @ GMAIL . COM

Bauhaus-Universität Weimar
Bauhausstr. 1199421 Weimar – Germany

Ron Weiss

RONWEISS @ GMAIL . COM

Google Inc
76 Ninth Avenue
New York, NY 10011 – USA

Vincent Dubourg

VINCENT. DUBOURG @ GMAIL . COM

Clermont Université, IFMA, EA 3867, LaMI
BP 10448, 63000 Clermont-Ferrand – FranceJake VanderplasVANDERPLAS @ ASTRO . WASHINGTON . EDU

Astronomy Department
University of Washington, Box 351580
Seattle, WA 98195 – USA

Alexandre Passos

ALEXANDRE . TP @ GMAIL . COM

IESL Lab
UMass Amherst
Amherst MA 01002 – USADavid Cournapeau

COURNAPE @ GMAIL . COM

Enthought
21 J.J. Thompson Avenue
Cambridge, CB3 0FA – UKc 2011 Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel,Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher,
Matthieu Perrot and Édouard Duchesnay

P EDREGOSA , VAROQUAUX , G RAMFORT ET AL .

Matthieu Brucher

MATTHIEU . BRUCHER @ GMAIL . COM

Total SA, CSTJF
avenue Larribau
64000 Pau – France

Matthieu PerrotÉdouard Duchesnay

MATTHIEU . PERROT @ CEA . FREDOUARD . DUCHESNAY @ CEA . FR

LNAO
Neurospin, Bât 145, CEA Saclay
91191 Gif sur Yvette – France

Editor: Mikio Braun

Abstract
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis isput on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic
and commercial settings. Source code, binaries, and documentation can be downloaded fromhttp://scikit-learn.sourceforge.net.
Keywords: Python, supervised learning, unsupervised learning, model selection

1. IntroductionThe Python programming language is establishing itself as one of the most popular languages forscientific computing. Thanks to its high-level interactive nature and its maturing ecosystem of scientific libraries, it is an appealing choice for algorithmic development and exploratory data analysis(Dubois, 2007; Milmann and Avaizis, 2011). Yet, as a general-purpose language, it is increasingly
used not only in academic settings but also in industry.
Scikit-learn harnesses this rich environment to provide state-of-the-art implementations of many
well known machine learning algorithms, while maintaining an easy-to-use interface tightly integrated with the Python language. This answers the growing need for statistical data analysis bynon-specialists in the software and web industries, as well as in fields outside of computer-science,such as biology or physics. Scikit-learn differs from other machine learning toolboxes in Python
for various reasons: i) it is distributed under the BSD license ii) it incorporates compiled code forefficiency, unlike MDP (Zito et al., 2008) and pybrain (Schaul et al., 2010), iii) it depends only on
numpy and scipy to facilitate easy distribution, unlike pymvpa (Hanke et al., 2009) that has optional
dependencies such as R and shogun, and iv) it focuses on imperative programming, unlike pybrain
which uses a data-flow framework. While the package is mostly written in Python, it incorporates
the C++ libraries LibSVM (Chang and Lin, 2001) and LibLinear (Fan et al., 2008) that provide reference implementations of SVMs and generalized linear models with compatible licenses. Binary
packages are available on a rich set of platforms including Windows and any POSIX platforms.
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Furthermore, thanks to its liberal license, it has been widely distributed as part of major free software distributions such as Ubuntu, Debian, Mandriva, NetBSD and Macports and in commercial
distributions such as the “Enthought Python Distribution”.

2. Project Vision
Code quality. Rather than providing as many features as possible, the project’s goal has been toprovide solid implementations. Code quality is ensured with unit tests—as of release 0.8, test
coverage is 81%—and the use of static analysis tools such as pyflakes and pep8. Finally, westrive to use consistent naming for the functions and parameters used throughout a strict adherence
to the Python coding guidelines and numpy style documentation.
BSD licensing. Most of the Python ecosystem is licensed with non-copyleft licenses. While suchpolicy is beneficial for adoption of these tools by commercial projects, it does impose some restrictions: we are unable to use some existing scientific code, such as the GSL.
Bare-bone design and API. To lower the barrier of entry, we avoid framework code and keep thenumber of different objects to a minimum, relying on numpy arrays for data containers.
Community-driven development. We base our development on collaborative tools such as git, github
and public mailing lists. External contributions are welcome and encouraged.
Documentation. Scikit-learn provides a ∼300 page user guide including narrative documentation,
class references, a tutorial, installation instructions, as well as more than 60 examples, some featuring real-world applications. We try to minimize the use of machine-learning jargon, while maintaining precision with regards to the algorithms employed.

3. Underlying Technologies
Numpy: the base data structure used for data and model parameters. Input data is presented as
numpy arrays, thus integrating seamlessly with other scientific Python libraries. Numpy’s viewbased memory model limits copies, even when binding with compiled code (Van der Walt et al.,2011). It also provides basic arithmetic operations.
Scipy: efficient algorithms for linear algebra, sparse matrix representation, special functions andbasic statistical functions. Scipy has bindings for many Fortran-based standard numerical packages,such as LAPACK. This is important for ease of installation and portability, as providing libraries
around Fortran code can prove challenging on various platforms.
Cython: a language for combining C in Python. Cython makes it easy to reach the performanceof compiled languages with Python-like syntax and high-level operations. It is also used to bindcompiled libraries, eliminating the boilerplate code of Python/C extensions.

4. Code Design
Objects specified by interface, not by inheritance. To facilitate the use of external objects withscikit-learn, inheritance is not enforced; instead, code conventions provide a consistent interface.
The central object is an estimator, that implements a fit method, accepting as arguments an input
data array and, optionally, an array of labels for supervised problems. Supervised estimators, such as
SVM classifiers, can implement a predict method. Some estimators, that we call transformers,for example, PCA, implement a transform method, returning modified input data. Estimators
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P EDREGOSA , VAROQUAUX , G RAMFORT ET AL .

Support Vector Classification
Lasso (LARS)Elastic Net
k-Nearest Neighbors
PCA (9 components)
k-Means (9 clusters)
License-: Not implemented.

scikit-learn
5.2
1.17
0.52
0.57
0.18
1.34
BSDmlpy
9.47
105.3
73.7
1.41
0.79
GPL

pybrain
17.5⋆
BSD

pymvpa mdp shogun
11.52
40.48
5.63
37.35
1.44
0.56
0.58
1.36
8.93
0.47
0.33
35.75
0.68
BSD
BSD
GPL
⋆: Does not converge within 1 hour.

Table 1: Time in seconds on the Madelon data set for various machine learning libraries exposedin Python: MLPy (Albanese et al., 2008), PyBrain (Schaul et al., 2010), pymvpa (Hankeet al., 2009), MDP (Zito et al., 2008) and Shogun (Sonnenburg et al., 2010). For more
benchmarks see http://github.com/scikit-learn.
may also provide a score method, which is an increasing evaluation of goodness of fit: a loglikelihood, or a negated loss function. The other important object is the cross-validation iterator,
which provides pairs of train and test indices to split input data, for example K-fold, leave one out,
or stratified cross-validation.
Model selection. Scikit-learn can evaluate an estimator’s performance or select parameters using
cross-validation, optionally distributing the computation to several cores. This is accomplished bywrapping an estimator in a GridSearchCV object, where the “CV” stands for “cross-validated”.
During the call to fit, it selects the parameters on a specified parameter grid, maximizing a score(the score method of the underlying estimator). predict, score, or transform are then delegatedto the tuned estimator. This object can therefore be used transparently as any other estimator. Cross
validation can be made more efficient for certain estimators by exploiting specific properties, suchas warm restarts or regularization paths (Friedman et al., 2010). This is supported through specialobjects, such as the LassoCV. Finally, a Pipeline object can combine several transformers andan estimator to create a combined estimator to, for example, apply dimension reduction before
fitting. It behaves as a standard estimator, and GridSearchCV therefore tune the parameters of all
steps.

5. High-level yet Efficient: Some Trade Offs
While scikit-learn focuses on ease of use, and is mostly written in a high level language, care has
been taken to maximize computational efficiency. In Table 1, we compare computation time for afew algorithms implemented in the major machine learning toolkits accessible in Python. We use
the Madelon data set (Guyon et al., 2004), 4400 instances and 500 attributes, The data set is quitelarge, but small enough for most algorithms to run.
SVM. While all of the packages compared call libsvm in the background, the performance of scikitlearn can be explained by two factors. First, our bindings avoid memory copies and have up to40% less overhead than the original libsvm Python bindings. Second, we patch libsvm to improve
efficiency on dense data, use a smaller memory footprint, and better use memory alignment andpipelining capabilities of modern processors. This patched version also provides unique features,such as setting weights for individual samples.
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S CIKIT- LEARN : M ACHINE L EARNING IN P YTHONLARS. Iteratively refining the residuals instead of recomputing them gives performance gains of
2–10 times over the reference R implementation (Hastie and Efron, 2004). Pymvpa uses this implementation via the Rpy R bindings and pays a heavy price to memory copies.
Elastic Net. We benchmarked the scikit-learn coordinate descent implementations of Elastic Net. Itachieves the same order of performance as the highly optimized Fortran version glmnet (Friedmanet al., 2010) on medium-scale problems, but performance on very large problems is limited since
we do not use the KKT conditions to define an active set.
kNN. The k-nearest neighbors classifier implementation constructs a ball tree (Omohundro, 1989)of the samples, but uses a more efficient brute force search in large dimensions.
PCA. For medium to large data sets, scikit-learn provides an implementation of a truncated PCA
based on random projections (Rokhlin et al., 2009).
k-means. scikit-learn’s k-means algorithm is implemented in pure Python. Its performance is limited by the fact that numpy’s array operations take multiple passes over data.

6. Conclusion
Scikit-learn exposes a wide variety of machine learning algorithms, both supervised and unsupervised, using a consistent, task-oriented interface, thus enabling easy comparison of methods for agiven application. Since it relies on the scientific Python ecosystem, it can easily be integrated intoapplications outside the traditional range of statistical data analysis. Importantly, the algorithms,
implemented in a high-level language, can be used as building blocks for approaches specific toa use case, for example, in medical imaging (Michel et al., 2011). Future work includes online
learning, to scale to large data sets.

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