![]() Hyperparameter tuning consists of finding a set of optimal hyperparameter values for a learning algorithm while applying this optimized algorithm to any data set. Different hyperparameter values produce different model parameter values for a given data set. We use hyperparameters to calculate the model parameters. Hyperparameters, on the other hand, are specific to the algorithm itself, so we can’t calculate their values from the data. For example, each weight and bias in a neural network is a parameter. After learning is complete, these parameters become part of the model. A learning algorithm learns or estimates model parameters for the given data set, then continues updating these values as it continues to learn. In machine learning, we need to differentiate between parameters and hyperparameters. Then, in the following two articles of this series, we’ll demonstrate how to tune hyperparameters on XGBoost and how to perform distributed hyperparameter tuning. In this article, we’ll explore some examples of hyperparameters and delve into a few models for tuning hyperparameters. In practice, key indicators like the accuracy or the confusion matrix will be worse. If we don’t correctly tune our hyperparameters, our estimated model parameters produce suboptimal results, as they don’t minimize the loss function. Hyperparameter tuning is an essential part of controlling the behavior of a machine learning model. If you're looking for a hands-on look at different tuning methods, be sure to check out part 2, How to tune hyperparameters on XGBoost, and part 3, How to distribute hyperparameter tuning using Ray Tune. □ This blog post is part 1 in our series on hyperparameter tuning.
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