hyperparams

tuner will work on the hyper-params of the model and tune the leaner model to get the best result

• Score functions are used to evaluate the performance of a model after it has been trained

• Unlike loss functions, which are minimized, score functions are maximized

• hyperparams are configurations that govern the training process itself. These are not learned from the data but are set prior to training and remain constant during the process. Hyperparameters include learning rate, the number of hidden layers in a neural network, the number of trees in a random forest, etc.