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A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts. [2] [3] Hyperparameter optimization determines the set of hyperparameters that yields an optimal model which minimizes a predefined loss function on a given data set. [4]
A good k can be selected by various heuristic techniques (see hyperparameter optimization). The special case where the class is predicted to be the class of the closest training sample (i.e. when k = 1) is called the nearest neighbor algorithm.
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).
As with the term hyperparameter, the use of hyper is to distinguish it from a prior distribution of a parameter of the model for the underlying system. They arise particularly in the use of hierarchical models. [1] [2] For example, if one is using a beta distribution to model the distribution of the parameter p of a Bernoulli distribution, then:
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
For instance, a data sample might be a natural language sentence, and the output could be an annotated parse tree. Training a classifier consists of showing many instances of ground truth sample-output pairs. After training, the SkNN model is able to predict the corresponding output for new, unseen sample instances; that is, given a natural ...
Hyperparameter may refer to: Hyperparameter (machine learning) Hyperparameter (Bayesian statistics) This page was last edited on 5 October 2024, at 04:17 (UTC). Text ...
In particular, the number of parameters used to describe the model is a poor measure of complexity. This is illustrated by an example adapted from: [ 6 ] The model f a , b ( x ) = a sin ( b x ) {\displaystyle f_{a,b}(x)=a\sin(bx)} has only two parameters ( a , b {\displaystyle a,b} ) but it can interpolate any number of points by ...