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Data Science Projects with Python

You're reading from Data Science Projects with Python

Product type Book

Published in Apr 2019

Publisher Packt

ISBN-13 9781838551025

Pages 374 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Stephen Klosterman

Table of Contents (9) Chapters

Data Science Projects with Python

Preface

1. Data Exploration and Cleaning

2. Introduction toScikit-Learn and Model Evaluation

3. Details of Logistic Regression and Feature Exploration

4. The Bias-Variance Trade-off

5. Decision Trees and Random Forests

6. Imputation of Missing Data, Financial Analysis, and Delivery to Client

Appendix

Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters

By now, you should be interested in using regularization in order to decrease the overfitting we observed when we tried to model the synthetic data in Exercise 17, 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...

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Authors (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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