Hyperparameter

A hyperparameter is a parameter of a machine learning model that is set prior to training the model. It is not learned from the training data and is often chosen by the practitioner based on prior knowledge or experimentation.

Hyperparameters are used to control the complexity and behavior of a machine learning model, and play a critical role in the performance of the model. They include parameters such as the learning rate, the regularization coefficient, and the size of the hidden layers in a neural network.

The values of the hyperparameters are typically chosen through a process called hyperparameter tuning or optimization, which involves adjusting the values of the hyperparameters to find the optimal combination that maximizes the performance of the model on a validation dataset. This process can be done manually, through a process of trial and error, or automatically, using techniques such as grid search or random search.

Hyperparameter tuning is an important step in the training and evaluation of a machine learning model, as it can have a significant impact on the performance of the model. It is important to carefully select the values of the hyperparameters to ensure that the model is able to generalize well to new data and achieve good performance on the task at hand.

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