How to Manually Optimize Machine Learning Model Hyperparameters
Machine learning algorithms have hyperparameters that allow the algorithms to be tailored to specific datasets.
Although the impact of hyperparameters may be understood generally, their specific effect on a dataset and their interactions during learning may not be known. Therefore, it is important to tune the values of algorithm hyperparameters as part of a machine learning project.
It is common to use naive optimization algorithms to tune hyperparameters, such as a grid search and a random search. An alternate approach is to use a stochastic optimization algorithm, like a stochastic hill climbing algorithm.
In this tutorial, you will discover how to manually optimize the hyperparameters of machine learning algorithms.
After completing this tutorial, you will know:
- Stochastic optimization