What are hyperparameters?

Hyperparameters are settings or configurations that are set before the training process begins and govern the training procedure and structure of the machine learning model. They include choices such as the learning rate, the number of hidden layers in a neural network, and the number of trees in a random forest. These parameters influence the model's performance and can significantly affect the outcome of the training process.

Choosing the right hyperparameters is crucial for achieving optimal model performance, as they can determine how well the model learns from the data and generalizes to unseen data. It is a fundamental part of the model optimization process, often involving techniques such as grid search or random search to find the most effective values.

In contrast, other options refer to different concepts within machine learning and data science. For instance, variables that change during training refer to parameters—the internal coefficients of the model that are adjusted through the training process. Metrics for evaluating model accuracy represent performance measures used after the model is built to assess its effectiveness. Lastly, components of a dataset pertain to the individual elements or features within the data being analyzed, rather than the settings that influence how a model is trained.