It is the process of selecting the optimal set of hyperparameters, for a machine learning model that maximizes its performance. Hyperparameters are the configuration variables that are not learned during the training of a model but rather set before the training process.Examples of hyperparameters include learning rate, regularization strength, number of hidden layers, number of nodes in each layer, batch size, etc.Hyperparameter optimization can be performed using various methods, some of which are:
Grid Search: Grid search is a technique that involves creating a grid of all possible combinations of hyperparameters and evaluating each combination using cross-validation. For example, suppose we have three hyperparameters: learning rate, batch size, and number of hidden layers. We can specify a range of values for each hyperparameter, such as learning rate = [0.01, 0.1, 1], batch size = [32, 64, 128], and number of hidden layers = [1, 2, 3]. Grid search will evaluate each combination of hyperparameters (9 in this example) and select the one that results in the best performance.
Random Search: Random search is a technique that involves randomly sampling hyperparameters from a specified distribution. For example, instead of specifying a grid, we can specify a probability distribution for each hyperparameter and randomly sample hyperparameters from these distributions. For example, we can specify a log-uniform distribution for the learning rate, a uniform distribution for the batch size, and a discrete distribution for the number of hidden layers. Random search will sample hyperparameters from these distributions and select the one that results in the best performance.
Bayesian Optimization: Bayesian optimization is a technique that uses a probabilistic model to guide the search for optimal hyperparameters. It involves constructing a prior distribution over the objective function and updating it as new observations are made. Bayesian optimization is computationally expensive but can be more efficient than grid search and random search for high-dimensional and non-convex optimization problems.
Evolutionary Algorithms: Evolutionary algorithms are a family of optimization techniques that simulate the process of natural selection. They involve creating a population of candidate solutions (i.e., hyperparameter configurations) and iteratively applying genetic operators (e.g., mutation, crossover) to generate new solutions. The fitness of each solution is evaluated using the objective function, and the best solutions are selected for the next generation. Evolutionary algorithms can be more efficient than grid search and random search for highly non-linear and non-convex optimization problems.