Hyperparameter tuning
Hyperparameter tuning is an important step in the machine learning process that involves selecting the best set of hyperparameters for a given model. Hyperparameters are values that are set before the training process begins and can have a significant impact on the model’s performance. Examples of hyperparameters include learning rate, regularization strength, number of hidden layers in a neural network, and many others.
The process of hyperparameter tuning involves selecting the best combination of hyperparameters that results in the optimal performance of the model. This is typically done by searching through a predefined set of hyperparameters and evaluating their performance on a validation set.
There are several methods for hyperparameter tuning, including grid search, random search, and Bayesian optimization. Grid search involves creating a grid of all possible hyperparameter combinations and evaluating each one on a validation set to determine...
