Hyperparameter tuning is the process of selecting the optimal values for the hyperparameters of a machine learning algorithm
Hyperparameters are different from model parameters. Model parameters are learned from the data during training, while hyperparameters are set by the user before training.
Hyperparameter tuning is crucial as the choice of hyperparameters can significantly impact the performance on the model.
Hyperparameters vary depending on the learning algorithm used
Hyperparameter tuning can be done manually,automatically or by adjusting hyperparameters based on intuition or domain knowledge.
A metric or scoring function is chosen to evaluate the performance of the model with different hyperparameter settings.
Hyperparameter tuning aims to strike a balance between underfitting and overfitting.
The right choice of hyperparameters can lead to significant improvements in a model's performance
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