the essential Machine Learning tool for Python

Scikit-Learn is the leading machine learning library in the Python language. Find out all you need to know about this essential tool and its many features!

All over the world, machine learning now plays a central role in many companies. It enables these organizations to take advantage of a competitive edge and to stand out from the crowd.

Among the various programming languages for implementing machine learning projects, Python is considered one of the most popular.

In addition to its simplicity, this language has the advantage of offering numerous libraries dedicated to machine learning. One of these is Scikit Learn, offering various machine learning and statistical modeling tools.

What is Scikit Learn?

Initially named scikits.learn, Scikit Learn was developed by David Cournapeau in 2007 as a Google Summer of Code (GSoC) project. Many volunteers then contributed, and the public launch took place on February 1, 2010.

This Python library enables build machine learning models. This is a NumFOCUS-funded project, open source and based on NumPy, SciPy and Matplotlib.

In addition, Scikit-Learn provides access to a large number of common machine learning algorithms. These features are accessible via a Python interface.

From many other tools are available for evaluation, selection, model development and data pre-processing. In addition, Sklearn integrates with a wide variety of Python libraries such as Matplotlib, Plotly, NumPy, Pandas and SciPy.

Although relatively new, this Python library has quickly become one of the most popular on GitHub. From many companies such as Spotify, Evernote, JP Morgan, Inria, AWeber and many others use it.

Features and functions

Sklearn is mainly used to model data. This library is open source and can be used commercially under a BSD license.

It offers clustering functionalities for grouping unlabeled data, and feature selection in order to identify useful attributes for the creation of supervised models.

L’feature extraction from data also enables attributes to be defined for image and text data. The cross-validation helps to verify the accuracy of supervised models on unpublished data.

The dimensionality reduction can be used to reduce the number of attributes in data, for synthesis, visualization and feature selection purposes. The ensemble methods can combine the predictions of multiple supervised models.

This library contains most supervised learning algorithms such as decision tree, linear regression, or support vector machines (SVM). All unsupervised learning algorithms are also included, including clustering, principal component analysis, factor analysis or unsupervised neural networks.

How to install Scikit Learn?

Before you start using scikit-learn, several prerequisites are essential. The latest versions of Python, Joblib, Scipy, NumPy, Matplotlib for dataviz and Pandas for data structure and analysis must be installed.

If you have already installed NumPy and SciPy, there are two easy ways to install Sklearn. The first is to use pip and the command “pip install -U scikit-learn”. The second relies on Conda and the “conda install scikit-learn” command.

If you don’t have NumPy or SciPy installed on your Python workstation, you can start by installing them with pip or conda. Alternatively, you can use Python distributions distributions such as Anaconda and Canopy, because they include the latest version of scikit-learn.