You're reading from Python Data Science Essentials. - Third Edition

Product typeBook

Published inSep 2018

Reading LevelIntermediate

PublisherPackt

ISBN-139781789537864

Edition3rd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Data Science

Author (1)

Alberto Boschetti

Preparing tools and datasets

As introduced in the previous chapters, the Python package for machine learning with the lion's share is scikit-learn. In this chapter, we also will use XGboost, LightGBM, and Catboost: you'll find the instructions in the relevant sections.

The motivations for using scikit-learn developed at Inria, the French Institute for Research in Computer Science and Automation (inria.fr/en/), are multiple. It is worthwhile at this point to mention the most important reasons for using scikit-learn for the success of your data science project:

A consistent API (fit, predict, transform, and partial_fit) across models that naturally helps to correctly implement data science procedures working on data organized in NumPy arrays
A complete selection of well-tested and scalable classical models for machine learning, offering many out-of-core implementations...

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Python Data Science Essentials. - Third Edition

Published in: Sep 2018Publisher: PacktISBN-13: 9781789537864

Author (1)

Alberto Boschetti

Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a Ph.D. in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges ranging from natural language processing (NLP) and behavioral analysis to machine learning and distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.
Read more about Alberto Boschetti

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