In this session, we explored the concept of Hyperparameter tuning, a key aspect in the development of machine learning models. Hyperparameters are essentially the adjustable parameters that we set prior to training a model. They function like the controls that dictate the model’s learning process and its eventual performance.
Some examples of hyperparameters include the learning rate, which determines the speed at which a model learns, the number of hidden layers in a neural network, the count of decision trees in a random forest model, and the degree of regularization used in linear regression.
The primary objective of hyperparameter tuning is to identify the optimal combination of these parameters that enables the machine learning model to perform at its best for a given task or dataset. This involves experimenting with various hyperparameter configurations to discover the one that yields the highest accuracy, minimizes errors, or produces the most favorable results for the particular challenge at hand. Through hyperparameter tuning, we enhance the model’s ability to make precise predictions on new, unseen data, thereby boosting its overall effectiveness and versatility.