enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Naive Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_classifier

    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.

  3. Hyperparameter (Bayesian statistics) - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_(Bayesian...

    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:

  4. Bayesian hierarchical modeling - Wikipedia

    en.wikipedia.org/wiki/Bayesian_hierarchical_modeling

    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)} .

  5. Bayesian optimization - Wikipedia

    en.wikipedia.org/wiki/Bayesian_optimization

    Bayesian optimization of a function (black) with Gaussian processes (purple). Three acquisition functions (blue) are shown at the bottom. [8]Bayesian optimization is typically used on problems of the form (), where is a set of points, , which rely upon less (or equal to) than 20 dimensions (,), and whose membership can easily be evaluated.

  6. Hyperparameter optimization - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_optimization

    A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts. [ 2 ] Hyperparameter optimization determines the set of hyperparameters that yields an optimal model which minimizes a predefined loss function on a given data set . [ 3 ]

  7. Multinomial distribution - Wikipedia

    en.wikipedia.org/wiki/Multinomial_distribution

    When k = 2, the multinomial distribution is the binomial distribution. Categorical distribution, the distribution of each trial; for k = 2, this is the Bernoulli distribution. The Dirichlet distribution is the conjugate prior of the multinomial in Bayesian statistics. Dirichlet-multinomial distribution. Beta-binomial distribution.

  8. Multiclass classification - Wikipedia

    en.wikipedia.org/wiki/Multiclass_classification

    In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification). For example, deciding on whether an image is showing a banana, an orange, or an ...

  9. Bayesian inference - Wikipedia

    en.wikipedia.org/wiki/Bayesian_inference

    Bayesian inference (/ ˈ b eɪ z i ə n / BAY-zee-ən or / ˈ b eɪ ʒ ən / BAY-zhən) [1] is a method of statistical inference in which Bayes' theorem is used to calculate a probability of a hypothesis, given prior evidence, and update it as more information becomes available.

  1. Related searches multinomial naive bayes hyperparameter tuning chart pdf print out online

    bayesian function optimizationbayesian optimization wikipedia
    bayesian optimization examples