Hyperparameter Tuning

Probabilistic models are estimated by unknown quantities, called parameters. These are adjusted using an optimization technique so that in the training sample it is possible to find a pattern in the best possible way. In a simple way, parameters are estimated by the algorithm and the user has little / nothing control over them. In a simple linear regression, the model parameters are betas (ẞ). BAM!!!: In statistics jargon, parameters are defined as population characteristics.