The open source project orchestrated by INRIA has become a reference machine learning infrastructure alongside deep learning frameworks such as Keras, Pytorch or Tensorflow.

What is Scikit-learn?

Initiated and piloted in France by INRIA and Télécom ParisTech, the Scikit-learn project has become a reference in the world of artificial intelligence. From Paris to San Francisco via Singapore, the open source Python machine learning library is a must for start-ups and large groups, including Gafam. It is available under the BSD license.

Scikit-learn covers the main generalist machine learning algorithms: classification, regression, clustering, gradient boosting… In parallel, the framework embeds NumPy, Matplotlib and SciPy, three star libraries of scientific computing which make it a very popular tool within of the community of researchers experienced in matrix computing.

What are the pros of Scikit-learn?

Among its differentiating factors, Scikit-learn is acclaimed for its cross-validation method. Upstream, it provides the possibility of very simply generating the training and test databases. Then, via a grid search mechanism, cross-validation makes it possible to find the model parameters that are closest to the expected predictions. The process adjusts the sampling of the test base by comparing it to the learning base by successive iterations (see diagram below). The objective is to achieve the correct setting in terms of thresholds, for example not to exceed 2% in terms of fraud detection.

Another strong point of Scikit-learn, the library embeds a whole range of methods to control the pre-processing of data sets upstream of the learning phases. They manage their extraction, cleaning, formatting and labeling. However, it offers limited integration with Pandas. A library which, during this delicate preparation stage, allows data to be manipulated in the form of tables rather than matrices as Scikit-learn does natively. The two libraries can nevertheless work together. A first gateway between Scikit-learn and Pandas is also available.

One of the main advantages of Scikit-learn is also to offer clear and didactic documentation with examples of implementations and ready-to-use packages. Tensorflow is much more difficult to configure. The open source nature of Scikit-learn (with community development as a bonus) and its ease of use have made it very popular.

What is the main limitation of Scikit-learn?

Scikit-learn suffers from a congenital weakness to Python technology. The latter being an interpreted language, the library cannot offer the performance of a compiled language. However, Python manages RAM much better than R, another star data science language, statistically oriented. The Cython partly solves the problem by opening up the possibility of compiling Python components in C or C++. A few algorithms are available in Scikit-learn for this language. This is the case of the SVM family (for Support Vector Machines editor’s note). Given this advantage, one could imagine that others will be implemented in the future by the community.

Which AI platforms integrate Scikit-learn?

Commercial AI tool vendors quickly saw Scikit-learn as a potential golden hen. The infrastructure is implemented by several data science heavyweights including the French Dataiku, the American DataRobot and the German Knime. It is also supported by a growing number of cloud players. This is the case of Google via its Cloud Machine Learning Engine service, IBM with Watson Machine Learning or even Microsoft through Azure Machine Learning.

How to download Scikit-learn on GitHub?

Scikit-learn is available on GitHub. You have to go to the machine learning framework page to access the file. The link makes it possible to distinguish the different modules, extensions and technical documentation available.

How to install Scikit-learn with PyPI?

The installation of Scikit-learn can be done from a Windows, Linux or macOS environment. In order to optimize the compatibility between system and framework, it is recommended to install it from a package manager designed under Python.

Under pip, which allows Python packages to be managed via PyPI (Python Package Index), the installation procedure requires the following line of code: “pip install -U scikit-learn“. The pip installation procedure and the corresponding Scikit-learn download files are accessible at this address. For an installation of Scikit-learn under Conda, you must go to this page. The system asks for the entry “conda install -c anaconda scikit-learn“.

Scikit-learn PCA

The Scikit-learn library allows you to perform a principal component analysis or principal component analysis (PCA). A method which consists in transforming correlated variables into new uncorrelated but less numerous variables, called principal components or principal axes.

The goal of principal component analysis is to reduce the volume of variables in the machine learning model. Clearly, it improves the performance of the model by eliminating variables that do not contribute to any decision-making.

What models are included in Scikit-learn?

In order to best manage data science projects, Scikit-learn includes several templates and features. We thus find:

  • Clustering: a data partitioning model that uses different algorithms, such as k-means or DBSCAN;
  • Linear regression: a calculation model that relies on a set of data to perform predictive functions;
  • The KNN classifier: compatible with the IRIS dataset, this is a model based on a supervised learning method;
  • Lasso: a statistical model that can be declined in a linear model or in vector writing. It implements a contraction technique on the regression coefficients.

Scikit-learn: documentation and tutorial

The official site of the Scikit-learn project offers a whole series of content to help users approach the machine learning library:


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