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In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts. [2] [3]
Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models. [4] Common techniques used in AutoML include hyperparameter optimization, meta-learning and neural architecture search.
In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters can be classified as either model hyperparameters (such as the topology and size of a neural network) or algorithm hyperparameters (such as the learning rate and the batch size of an optimizer).
In Bayesian statistics, a hyperparameter is a parameter of a prior distribution; the term is used to distinguish them from parameters of the model for the underlying system under analysis. For example, if one is using a beta distribution to model the distribution of the parameter p of a Bernoulli distribution , then:
Vowpal wabbit has been used to learn a tera-feature (10 12) data-set on 1000 nodes in one hour. [1] Its scalability is aided by several factors: Out-of-core online learning: no need to load all data into memory; The hashing trick: feature identities are converted to a weight index via a hash (uses 32-bit MurmurHash3)
[3] [4] For example, in multilayer perceptrons, the same function is preserved when permuting the nodes of a hidden layer, amounting to permuting weight matrices of the network. This property is known as equivariance to permutation of deep weight spaces. [3] The study seeks hyperparameter optimization.
Areas of the search space with a better performance are searched more likely. At the same time, other areas are further explored. The example shows that most of the 100 trials are close to the optimum (better performance = blue). In contrast to grid search and random search, better hyperparameter values are found in fewer trials.
In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. [1]