Hyperparameter tuning is the process of optimizing the parameters that govern the learning process of a machine learning model, which are not learned from the data itself. Effective tuning can significantly improve model performance by finding the optimal combination of hyperparameters for a given task.

Hyperparameter Tuning

Hyperparameter tuning

optimizing the parameters

learning process

machine learning model

Model performance refers to the effectiveness of a machine learning model in making accurate predictions or classifications based on the data it was trained on. It is typically evaluated using various metrics that assess how well the model generalizes to new, unseen data.

model performance

optimal combination of hyperparameters

given task

Hyperparameter Tuning: Optimizing Neural Network Performance

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