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2019 | OriginalPaper | Chapter

11. Complexity and Heuristics

Author : Ervin Varga

Published in: Practical Data Science with Python 3

Publisher: Apress

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Abstract

As a data scientist, you must solve essential problems in an economical fashion, taking into account all constraints and functional requirements. The biggest mistake is to get emotionally attached to some particular technique and try to find “suitable” problems to which to apply it; this is pseudo-science, at best. A similar issue arises when you attempt to apply some convoluted approach where a simpler and more elegant method exists. I’ve seen these two recurring themes myriad times. For example, logistic regression (an intuitive technique) often can outperform kernelized support vector machines in classification tasks, and a bit of cleverness in feature engineering may completely obviate the need for an opaque neural network–oriented approach. Start simple and keep it simple is perhaps the best advice for any data scientist. In order to achieve simplicity, you must invest an enormous amount of energy and time, but the end result will be more than rewarding (especially when including long-term benefits). Something that goes along with simplicity is pragmatism—good enough is usually better than perfect in most scenarios. This chapter will exemplify an attitude toward simplicity and pragmatism as well as acquaint you with the notion of complexity and heuristic. It contains many small use cases, each highlighting a particular topic pertaining to succinctness and effectiveness of a data product.

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Metadata
Title
Complexity and Heuristics
Author
Ervin Varga
Copyright Year
2019
Publisher
Apress
DOI
https://doi.org/10.1007/978-1-4842-4859-1_11

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