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The values of the hyperparameters regulate the learning process, while also deciding the values of the model parameters that a learning algorithm ultimately learns. The learning process and the model parameters that are produced as a consequence of it are both under the control of hyperparameters which are high-level parameters.

Before machine learning model training can even begin, data scientists must first choose and configure the hyperparameter values that the learning algorithm will utilize. This is done as part of the machine learning model training process. In this context, hyperparameters are considered to exist outside of the model because the model’s values cannot be altered during the learning or training process.