What is Hyperparameter Tuning?
Hyperparameter tuning is the process of selecting the best set of hyperparameters for a machine learning model. Hyperparameters are the parameters of the learning algorithm itself, as opposed to the model parameters that are learned during the training process. Examples of hyperparameters include the learning rate, regularization strength, and the number of hidden layers in a neural network. Hyperparameter tuning aims to optimize the model’s performance on a given task by searching through a range of possible hyperparameter values and selecting the combination that achieves the best results.
What is the difference between Hyperparameter and parameters?
A learning model estimates model parameters for the given dataset, and then continually updates these values. Now, after learning is complete, these parameters become part of the model.
On the other hand, Hyperparameters are specific to the algorithm itself and they cannot be calculated or estimated from the data. They influence the learning process of the algorithm and are always configured before starting the model learning process.
Hyperparameters are used to calculate the model parameters and different hyperparameters produce different parameter values for a given dataset.
Common Hyperparameter Tuning Techniques
Grid Search: Systematically search through a predefined set of hyperparameter values, evaluating the model performance for each combination.
Random Search: Randomly sample hyperparameter values from a specified distribution, exploring a larger search space more efficiently.
Bayesian Optimization: Model the relationship between hyperparameters and model performance using a probabilistic model, allowing for efficient exploration and exploitation of the search space.