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Automated Machine Learning Hyperparameter Tuning in Python

This article was written by Will Koehrsen.

Full titleAutomated Machine Learning Hyperparameter Tuning in Python - A complete walk through using Bayesian optimization for automated hyperparameter tuning in Python

Tuning machine learning hyperparameters is a tedious yet crucial task, as the  on the choice of hyperparameters. Manual tuning takes time away from important steps of the machine learning pipeline like  and . Grid and  are hands-off, but require long run times because they waste time evaluating unpromising areas of the search space. Increasingly, hyperparameter tuning is done by automated methods that aim to find optimal hyperparameters in less time using an informed search with no manual effort necessary beyond the initial set-up.

, a model-based method for finding the minimum of a function, has recently been applied , with  suggesting this approach can achieve better performance on the test set while requiring fewer iterations than random search. Moreover, there are now a number of Python libraries that make implementing Bayesian hyperparameter tuning simple for any machine learning model.

In this article, we will walk through a complete example of Bayesian hyperparameter tuning of a gradient boosting machine using the . In an  behind this method, so here we will stick to the implementation. Like with most machine learning topics, it’s not necessary to understand all the details, but knowing the basic idea can help you use the technique more effectively!

All the code for this article is available as a .

Table of Contents

  • Bayesian Optimization Methods
  • Four Parts of Optimization Problem
  • Objective Function
  • Domain Space
  • Optimization Algorithm
  • Result History
  • Optimization
  • Results
  • Visualizing Search Results
  • Evolution of Search
  • Continue Searching
  • Conclusions

To read this article, click here.

 

 

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