Machine learning models have hyperparameters that you must set in order to customize the model to your dataset.

Often the general effects of hyperparameters on a model are known, but how to best set a hyperparameter and combinations of interacting hyperparameters for a given dataset is challenging. There are often general heuristics or rules of thumb for configuring hyperparameters.

A better approach is to objectively search different values for model hyperparameters and choose a subset that results in a model that achieves the best performance on a given dataset. This is called hyperparameter optimization or hyperparameter tuning and is available in the scikit-learn Python machine learning library. The result of a hyperparameter optimization is a single set of well-performing hyperparameters that you can use to configure your model.

In this tutorial, you will discover hyperparameter optimization for machine learning in Python.

After completing this tutorial, you will know:

• Hyperparameter optimization is required to get the most out of your machine learning models.
• How to configure random and grid search hyperparameter optimization for classification tasks.
• How to configure random and grid search hyperparameter optimization for regression tasks.

Let’s get started.

#python machine learning #machine-learning