Machine Learning

Scikit-Optimize for Hyperparameter Tuning in Machine Learning

Last Updated on September 7, 2020

Hyperparameter optimization refers to performing a search in order to discover the set of specific model configuration arguments that result in the best performance of the model on a specific dataset.

There are many ways to perform hyperparameter optimization, although modern methods, such as Bayesian Optimization, are fast and effective. The Scikit-Optimize library is an open-source Python library that provides an implementation of Bayesian Optimization that can be used to tune the hyperparameters of machine learning models from the scikit-Learn Python library.

You can easily use the Scikit-Optimize library to tune the models on your next machine learning project.

In this tutorial, you will discover how to use the Scikit-Optimize library to use Bayesian Optimization for hyperparameter tuning.

After completing this tutorial, you will know:

  • Scikit-Optimize provides a general toolkit for Bayesian Optimization that can be used for hyperparameter tuning.
  • How to manually use the Scikit-Optimize library to tune the hyperparameters of a machine learning model.
  • How to use the built-in BayesSearchCV class to perform model hyperparameter tuning.

Let’s get started.

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