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While naive Bayes often fails to produce a good estimate for the correct class probabilities, [16] this may not be a requirement for many applications. For example, the naive Bayes classifier will make the correct MAP decision rule classification so long as the correct class is predicted as more probable than any other class. This is true ...
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:
The parameter is called the hyperparameter, while its distribution given by (,) is an example of a hyperprior distribution. The notation of the distribution of Y changes as another parameter is added, i.e. Y ∣ θ , μ ∼ N ( θ , 1 ) {\displaystyle Y\mid \theta ,\mu \sim N(\theta ,1)} .
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]
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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]
This means that if a data point has either a categorical or multinomial distribution, and the prior distribution of the distribution's parameter (the vector of probabilities that generates the data point) is distributed as a Dirichlet, then the posterior distribution of the parameter is also a Dirichlet. Intuitively, in such a case, starting ...
Nvidia stock led gains among the “Magnificent Seven” tech stocks to start the new year after a group-wide sell-off in the last days of 2024.Shares of the AI chipmaker rose 4.5% Friday after ...