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###### **Hyperparameters and hyperparameter tuning**

The model training process is controlled by a type of variables known as *hyperparameters*. 

Hyperparameter values are *not acquired* through the learning process. Unlike node weights and other training parameters, the values of hyperparameters remain *unchanged* during the model training process.

The process of *hyperparameter tuning* involves optimizing the values of hyperparameters to maximize the predictive accuracy of the model. If you are not using Katib or a similar system for hyperparameter tuning, you would need to manually run multiple training jobs and adjust the hyperparameters yourself in order to find the optimal values.

Automated hyperparameter tuning works by optimizing a specified target variable, known as the objective metric, which is defined in the configuration for the hyperparameter tuning job. A commonly used metric is the model's accuracy during the validation phase of the training job, often referred to as validation-accuracy. Additionally, you can indicate whether you want the hyperparameter tuning job to *maximize* or *minimize* the metric in question.  

For example, the following graph from Katib shows the level of Mean Absolute Percentage Error (MAPE) metric for various combinations of hyperparameter values:
- the batch size
- the number of layers
- the number of hidden units
- the optimizer
- the window size

![MarineGEO circle logo](/images/UI/hyperparameters_plot_example.png#hyperparameters_plot_example)

###### **Next steps**

Follow [Optimization Algorithms section](/docs/components/hpo/algorithms) to know more about details of search algorithms.

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