Hyper Parameter Tuning with Uninformed and Informed Search

Hyperparameters are those parameters in Machine learning algorithms that are used to control the learning process of algorithms. Hyperparameter tuning is the process of finding the best hyperparameters which help us to build more accurate machine learning models. Note: There is a difference between Model Parameters and Hyper Parameters. Model parameters are learned from data e.g. Slope and intercept in Linear Regression models, and Hyperparameters are those which we set such as L1 or L2 Regularization in Regression Model.