A Gentle Introduction to Scikit-Learn: A Python Machine Learning Library

If you are a Python programmer or you are looking for a robust library you can use to bring machine learning into a production system then a library that you will want to seriously consider is scikit-learn.

In this post you will get an overview of the scikit-learn library and useful references of where you can learn more.

Where did it come from?

Scikit-learn was initially developed by David Cournapeau as a Google summer of code project in 2007.

Later Matthieu Brucher joined the project and started to use it as apart of his thesis work. In 2010 INRIA got involved and the first public release (v0.1 beta) was published in late January 2010.

The project now has more than 30 active contributors and has had paid sponsorship from INRIA, Google, Tinyclues and the Python Software Foundation.

What is scikit-learn?

Scikit-learn provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python.

It is licensed under a permissive simplified BSD license and is distributed under many Linux distributions, encouraging academic and commercial use.

The library is built upon the SciPy (Scientific Python) that must be installed before you can use scikit-learn. This stack that includes:

  • NumPy: Base n-dimensional array package
  • SciPy: Fundamental library for scientific computing
  • Matplotlib: Comprehensive 2D/3D plotting
  • IPython: Enhanced interactive console
  • Sympy: Symbolic mathematics
  • Pandas: Data structures and analysis

Extensions or modules for SciPy care conventionally named SciKits. As such, the module provides learning algorithms and is named scikit-learn.

The vision for the library is a level of robustness and support required for use in production systems. This means a deep focus on concerns such as easy of use, code quality, collaboration, documentation and performance.

Although the interface is Python, c-libraries are leverage for performance such as numpy for arrays and matrix operations, LAPACK, LibSVM and the careful use of cython.

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What are the features?

The library is focused on modeling data. It is not focused on loading, manipulating and summarizing data. For these features, refer to NumPy and Pandas.

Some popular groups of models provided by scikit-learn include:

  • Clustering: for grouping unlabeled data such as KMeans.
  • Cross Validation: for estimating the performance of supervised models on unseen data.
  • Datasets: for test datasets and for generating datasets with specific properties for investigating model behavior.
  • Dimensionality Reduction: for reducing the number of attributes in data for summarization, visualization and feature selection such as Principal component analysis.
  • Ensemble methods: for combining the predictions of multiple supervised models.
  • Feature extraction: for defining attributes in image and text data.
  • Feature selection: for identifying meaningful attributes from which to create supervised models.
  • Parameter Tuning: for getting the most out of supervised models.
  • Manifold Learning: For summarizing and depicting complex multi-dimensional data.
  • Supervised Models: a vast array not limited to generalized linear models, discriminate analysis, naive bayes, lazy methods, neural networks, support vector machines and decision trees.

Example: Classification and Regression Tress

I want to give you an example to show you how easy it is to use the library.

In this example, we use the Classification and Regression Tress (CART) decision tree algorithm to model the Iris flower dataset.

This dataset is provided as an example dataset with the library and is loaded. The classifier is fit on the data and then predictions are made on the training data.

Finally, the classification accuracy and a confusion matrix is printed.

Running this example produces the following output, showing you the details of the trained model, the skill of the model according to some common metrics and a confusion matrix.

Who is using it?

The scikit-learn testimonials page lists Inria, Mendeley, wise.io , Evernote, Telecom ParisTech and AWeber as users of the library.

If this is a small indication of companies that have presented on their use, then there are very likely tens to hundreds of larger organizations using the library.

It has good test coverage and managed releases and is suitable for prototype and production projects alike.


If you are interested in learning more, checkout the Scikit-Learn homepage that includes documentation and related resources.

You can get the code from the github repository, and releases are historically available on the Sourceforge project.


I recommend starting out with the quick-start tutorial and flicking through the user guide and example gallery for algorithms that interest you.

Ultimately, scikit-learn is a library and the API reference will be the best documentation for getting things done.


If you interested in more information about how the project started and it’s vision, there are some papers you may want to check-out.


If you are looking for a good book, I recommend “Building Machine Learning Systems with Python”. It’s well written and the examples are interesting.

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7 Responses to A Gentle Introduction to Scikit-Learn: A Python Machine Learning Library

  1. Joe McCarthy April 19, 2014 at 1:09 am #

    This is a great overview of scikit-learn.

    I recently learned about IPython Notebooks during a Strata 2014 session by Brian Granger, and have since found lots of valuable pythonic and machine learning resources provided through notebooks posted on GitHub and/or hosted on ipython.org.

    Here are two I would recommend:

    PyCon 2014 Scikit-learn Tutorial by Jake VanderPlas

    Parallel Machine Learning with scikit-learn and IPython by Olivier Grisel (also offered at Strata 2014)

    FWIW, I put together my own IPython Notebook on Python for Data Science, designed to provide a rapid on-ramp primer for people with knowledge of other programming languages to learn enough about Python to effectively use scikit-learn and other more advanced machine learning and scientific computing tools.

    • jasonb April 19, 2014 at 5:20 am #

      Hey Joe, thanks for the links mate.

      Your own Python for Data Science notebook is amazing. It’s going to take me some time to digest fully. Thanks for sharing!

  2. Martin May 8, 2014 at 5:50 am #

    Two corrections:
    It’s matplotlib not mathplotlib and that’ll do 3d plots as well as 2d.

  3. jai March 27, 2015 at 10:18 pm #

    Thanks jasonb for providing such valuable tutorial on ML,

    Basically i am a biologist and from past 1-2 year i am getting involved my self in machine learning. Presently i am dealing with scikit-learn and has some previus experince with WEKA 6, which is a best open source GUI based tool for ML as best of my undestaing. In scikit-learn i m strugling badely at one point i.e. feature selection, if i compre with weka, it provides various feature selection methods and result gives you a list of selected descriptos which can be saved easily in the form of reduced data.

    Can you provide me any suggestion, how can i perform same task in Scikit-learn feature selection methods and can come up wiht the list of the names of selelcted features.


  4. MB August 28, 2015 at 7:52 am #

    Hi Jason,

    I am wondering if you run into this before. We have trained a model with training data and tested with test data of 100 instances for example and we got around 70% accuracy. Interesting aspect of scikit learn is that the predict function takes n_samples, this is fine when we are building and testing a model. But if I had to take this to production, I am having issues:
    1. I can send only single request (instance) at a time. 2. If we test record by record, our accuracy drops to 30%. Do you have any idea why?

  5. Robin White January 21, 2016 at 3:39 pm #

    And I would also like to introduce the course you can learn machine learning in Python http://www.thedevmasters.com/machine-learning-using-python/ I have taken that course before, then I could build my own library of Python scripts. I am sure that you will be satisfied with this bootcamp!

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