All of today’s leading companies make machine learning a central part of their operations. It has become a significant competitive differentiator for most organizations. To implement machine learning projects, one of the most popular programming languages out there is Python. Python’s simplicity allows you to work on complex algorithms and versatile workflows without focusing too much on the technical nuances of the language. Scikit learn is a robust library available in Python that provides a selection of tools for machine learning and statistical modeling. But what is scikit learn? Let’s discuss the basics of this popular Python package in this article.

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What Is Scikit Learn?

Scikit Learn or Sklearn is one of the most robust libraries for machine learning in Python. It is open source and built upon NumPy, SciPy, and Matplotlib. It provides a range of tools for machine learning and statistical modeling including dimensionality reduction, clustering, regression, and classification, through a consistent interface in Python. Additionally, it provides many other tools for evaluation, selection, model development, and data preprocessing. 

Scikit-learn is one of NumFOCUS’s fiscally sponsored projects. It also integrates well with many other Python libraries, such as Matplotlib, Plotly, NumPy, Pandas, SciPy, etc. Although the library is fairly new, it has quickly become one of the most popular libraries on GitHub. A number of big organizations such as Spotify, Evernote, JP Morgan, Inria, AWeber, and many more use Sklearn.

Note: Sklearn is used to build machine learning models.

Origin of Scikit Learn

Scikit Learn was originally called scikits.learn. It was developed by David Cournapeau as a Google Summer of Code (GSoC) project in 2007. The project was taken to another level by a number of volunteers and was first made public on 1st Feb 2010.

Here is a full rundown of the different versions of Scikit Learn:

  • August 2013 - scikit-learn 0.14
  • July 2014 - scikit-learn 0.15.0
  • March 2015 - scikit-learn 0.16.0
  • November 2015 - scikit-learn 0.17.0
  • September 2016 - scikit-learn 0.18.0
  • July 2017 - scikit-learn 0.19.0
  • July 2018 - scikit-learn 0.19.2
  • September 2018 - scikit-learn 0.20.0
  • November 2018 - scikit-learn 0.20.1
  • December 2018 - scikit-learn 0.20.2
  • March 2019 - scikit-learn 0.20.3
  • May 2019 - scikit-learn 0.21.0
  • December 2019 - scikit-learn 0.22.0
  • May 2020 - scikit-learn 0.23.0
  • Jan 2021 - scikit-learn 0.24
  • September 2021 - scikit-learn 1.0

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Community and Contributors of Sklearn

One of the main reasons behind the popularity of Sklearn is the community and contributors behind it. Since it is open-source, anyone can contribute to it. The following people are currently the core contributors to scikit-learn’s development and maintenance:

  • Jérémie du Boisberranger 
  • Joris Van den Bossche 
  • Loïc Estève 
  • Thomas J. Fan 
  • Alexandre Gramfort 
  • Olivier Grisel 
  • Yaroslav Halchenko 
  • Nicolas Hug 
  • Adrin Jalali 
  • Julien Jerphanion 
  • Guillaume Lemaitre 
  • Christian Lorentzen 
  • Jan Hendrik Metzen 
  • Andreas Mueller 
  • Vlad Niculae 
  • Joel Nothman 
  • Hanmin Qin 
  • Bertrand Thirion 
  • Tom Dupré la Tour 
  • Gael Varoquaux 
  • Nelle Varoquaux 
  • Roman Yurchak

In addition to these contributors and communities, there are also various meetups held across the globe. There was a Kaggle knowledge contest hosted recently to encourage people to start playing around with the library. The overall governance structure and decision-making process of scikit-learn are laid out in the governance document.

Prerequisites for Sklearn

Before you start using scikit-learn, you would require the following:

  • Python (version 3.5 or higher)
  • Joblib (version 0.11 or higher)
  • Scipy (version 0.17.0 or higher)
  • NumPy (version 1.11.0 or higher)
  • Matplotlib (version 1.5.1 or higher) for plotting capabilities
  • Pandas (version 0.18.0 or higher) for some of the Sklearn examples using data structure and analysis.

If you are new to any of these concepts, we recommend you learn them first before you dig further into Sklearn.

How to Install Sklearn

If you have already installed NumPy and Scipy, you can install scikit-learn in two easy methods:

Method 1 - Using Pip

Use the following command to install scikit-learn using pip:

Scikit_Learn_1

Method 2 - Using Conda

Use the following command to install scikit-learn using conda:

Scikit_Learn_2

If you do not have NumPy and Scipy installed on you Python workstation, you can install them first by using either pip or conda. Another alternative is to use Python distributions such as Anaconda and Canopy as they both ship the latest version of scikit-learn.

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Features of Sklearn

The Scikit-learn library is focused on modeling data. Some of the most popular features provided by Sklearn are:

  • Open Source − It is an open-source library and commercially usable under the BSD license.

  • Clustering − It can be used for grouping unlabeled data.

  • Supervised Learning algorithms − It contains almost all the popular supervised learning algorithms such as Decision Tree, Linear Regression, Support Vector Machine (SVM), etc.
  • Unsupervised Learning algorithms − It also contains all the popular unsupervised learning algorithms such as clustering, principal component analysis, factor analysis, unsupervised neural networks, etc.
  • Feature selection − It can identify useful attributes to create supervised models.
  • Feature extraction − It can extract features from data to define the attributes in image and text data.
  • Cross-Validation − It can check the accuracy of supervised models on unseen data.
  • Dimensionality Reduction − It can reduce the number of attributes in data which can be further used for summarization, visualization, and feature selection.
  • Ensemble methods − It can combine the predictions of multiple supervised models.
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Want to Learn More?

Scikit-learn is probably the most useful and robust library available in Python for machine learning. The library is continuously being developed and improved by contributors worldwide. If you are interested to learn more about what is scikit learn and how to use it for your machine learning projects, you can check out Simplilearn’s Data Science Certification Program. The program has been created in partnership with Purdue University and in collaboration with IBM and features masterclasses by Purdue faculty and IBM experts, exclusive hackathons, and Ask Me Anything sessions by IBM. Sign up for this course today and accelerate your career in data science.

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