Understanding Hyperparameters in Machine Learning #ai #artificialintelligence #machinelearning

@genaiexp Hyperparameters are settings that influence how a machine learning model learns from data. They include elements like learning rate, number of layers in a neural network, and the number of trees in a random forest. These are different from model parameters, such as weights in a neural network, which are learned during the training process. Hyperparameters must be set before training begins and can significantly influence the efficiency and accuracy of a model. For instance, a learning rate that's too high can cause a model to converge too quickly to a suboptimal solution, while one too low can slow down the learning process. Therefore, tuning these hyperparameters is essential to ensure optimal model performance.