Electronic Resource

Automated Machine Learning

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A machine learning engineer, or a data scientist, when building the machine learning pipeline for a specific task has to carefully design each of these steps. These steps are usually co-dependent. To give an example, consider a problem where the use of SVMs are desirable in building the model. Then, since SVMs cannot work natively with categorical features, these have to be transformed in some way, for example by one hot encoding, to numerical features. In this case, the model selection affects how certain features are encoded.

Designing and optimizing these steps require a deep knowledge on a wide range of algorithms, their strengths and weaknesses, hyperparameters of algorithms, and the encoding of data for these algorithms to work well. In a technological landscape where AI is being integrated into many fields, there exists a deficit of data scientists with enough expertise to analyze diverse sets of data and build machine learning models.

In an effort to make machine learning more accessible, to reduce the human expertise required, and to improve model performance, automated machine learning emerged as an exciting new area of active research.

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Series Title

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Publisher

: Springer International Publishing., 2019

Collation

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Language

ISBN/ISSN

9783030053185

Classification

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Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

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Subject(s)

Specific Detail Info

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Statement of Responsibility

Frank Hutter, Lars Kotthoff, Joaquin Vanschoren

Other Information

Cataloger

Siti Muawanah

Source

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Validator

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Digital Object Identifier (DOI)

https://softwareengineeringdaily.com/2019/05/15/introduction-to-automated-machine-learning-automl/

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