Hyperparameter tuning is an essential practice in optimizing the performance of machine learning models. This article provides an in-depth exploration of advanced hyperparameter tuning methods, including Population-Based Training (PBT), BOHB, ASHA, TPE, Optuna, DEHB, Meta-Gradient Descent, BOSS, and SNIPER. Before delving into these methods, let's establish a foundational understanding of hyperparameters, their distinction from model parameters, and their role within the machine learning lifecycle.
What Are Hyperparameters? How Do They Differ From Model Parameters?
In the realm of machine learning, model parameters are internal variables that a model learns from training data, such as the weights and biases of a neural network. Hyperparameters, on the other hand, are external settings that govern the learning process itself. These settings are determined before training and include factors like learning rates, batch sizes, and the number of hidden layers. While model parameters adapt through training, hyperparameters remain fixed throughout the training process.