Understanding Hyperparameters #ai #artificialintelligence #machinelearning #aiagent #Understanding

@genaiexp Before we delve into Grid Search, let's understand what hyperparameters are. Hyperparameters are the external configurations of a machine learning algorithm, in contrast to model parameters which are learned from the training data. Examples include the learning rate for training a neural network or the number of trees in a random forest. Unlike model parameters, hyperparameters are not learned during training but set prior to the training process. The right choice of hyperparameters can drastically improve your model's performance, making their optimization a critical step in the machine learning pipeline. Hence, mastering techniques like Grid Search is invaluable.