You're reading from Python Machine Learning - Third Edition

Product typeBook

Published inDec 2019

Reading LevelExpert

PublisherPackt

ISBN-139781789955750

Edition3rd Edition

Languages

Python

Tools

TensorFlow Keras

Concepts

Machine Learning

Authors (2):

Sebastian Raschka

Vahid Mirjalili

View More author details

Choosing a classification algorithm

Choosing an appropriate classification algorithm for a particular problem task requires practice and experience; each algorithm has its own quirks and is based on certain assumptions. To restate the no free lunch theorem by David H. Wolpert, no single classifier works best across all possible scenarios (The Lack of A Priori Distinctions Between Learning Algorithms, Wolpert, David H, Neural Computation 8.7 (1996): 1341-1390). In practice, it is always recommended that you compare the performance of at least a handful of different learning algorithms to select the best model for the particular problem; these may differ in the number of features or examples, the amount of noise in a dataset, and whether the classes are linearly separable or not.

Eventually, the performance of a classifier—computational performance as well as predictive power—depends heavily on the underlying data that is available for learning. The five main steps that...

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Python Machine Learning - Third Edition

Published in: Dec 2019Publisher: PacktISBN-13: 9781789955750

Authors (2)

Sebastian Raschka

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.
Read more about Sebastian Raschka

Vahid Mirjalili

Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.
Read more about Vahid Mirjalili

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You're reading from Python Machine Learning - Third Edition

Choosing a classification algorithm

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