You're reading from Data Science Projects with Python - Second Edition

Product typeBook

Published inJul 2021

Reading LevelIntermediate

PublisherPackt

ISBN-139781800564480

Edition2nd Edition

Languages

Python

Concepts

Data Science

Author (1)

Stephen Klosterman

Cross-Validation: Choosing the Regularization Parameter

By now, you may suspect that we could use regularization in order to decrease the overfitting we observed when we tried to model the synthetic data in Exercise 4.02, Generating and Modeling Synthetic Classification Data. The question is, how do we choose the regularization parameter, C? C is an example of a model hyperparameter. Hyperparameters are different from the parameters that are estimated when a model is trained, such as the coefficients and the intercept of a logistic regression. Rather than being estimated by an automated procedure like the parameters are, hyperparameters are input directly by the user as keyword arguments, typically when instantiating the model class. So, how do we know what values to choose?

Hyperparameters are more difficult to estimate than parameters. This is because it is up to the data scientist to determine what the best value is, as opposed to letting an optimization algorithm find it. However...

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Data Science Projects with Python - Second Edition

Published in: Jul 2021Publisher: PacktISBN-13: 9781800564480

Author (1)

Stephen Klosterman

Stephen Klosterman is a Machine Learning Data Scientist with a background in math, environmental science, and ecology. His education includes a Ph.D. in Biology from Harvard University, where he was an assistant teacher of the Data Science course. His professional experience includes work in the environmental, health care, and financial sectors. At work, he likes to research and develop machine learning solutions that create value, and that stakeholders understand. In his spare time, he enjoys running, biking, paddleboarding, and music.
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Cross-Validation: Choosing the Regularization Parameter

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