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Machine Learning for Cybersecurity Cookbook
book

Machine Learning for Cybersecurity Cookbook

by Emmanuel Tsukerman
November 2019
Intermediate to advanced content levelIntermediate to advanced
346 pages
9h 36m
English
Packt Publishing
Content preview from Machine Learning for Cybersecurity Cookbook

Hyperparameter tuning with scikit-optimize

In machine learning, a hyperparameter is a parameter whose value is set before the training process begins. For example, the choice of learning rate of a gradient boosting model and the size of the hidden layer of a multilayer perceptron, are both examples of hyperparameters. By contrast, the values of other parameters are derived via training. Hyperparameter selection is important because it can have a huge effect on the model's performance.

The most basic approach to hyperparameter tuning is called a grid search. In this method, you specify a range of potential values for each hyperparameter, and then try them all out, until you find the best combination. This brute-force approach is comprehensive ...

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Publisher Resources

ISBN: 9781789614671Supplemental Content