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Example of a naive Bayes classifier depicted as a Bayesian Network. In statistics, naive Bayes classifiers are a family of linear "probabilistic classifiers" which assumes that the features are conditionally independent, given the target class. The strength (naivety) of this assumption is what gives the classifier its name.
In statistical classification, the Bayes classifier is the classifier having the smallest probability of misclassification of all classifiers using the same set of features. [ 1 ] Definition
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The simplest one is Naive Bayes classifier. [2] Using the language of graphical models, the Naive Bayes classifier is described by the equation below. The basic idea (or assumption) of this model is that each category has its own distribution over the codebooks, and that the distributions of each category are observably different.
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]
In this example, the posterior probability mass is evenly split between the values 0.08 and 0.43. The posterior probabilities are obtained via ABC with large n {\displaystyle n} by utilizing the summary statistic (with ϵ = 0 {\displaystyle \epsilon =0} and ϵ = 2 {\displaystyle \epsilon =2} ) and the full data sequence (with ϵ = 0 ...
Standard examples of each, all of which are linear classifiers, are: generative classifiers: naive Bayes classifier and; linear discriminant analysis; discriminative model: logistic regression; In application to classification, one wishes to go from an observation x to a label y (or probability distribution on labels
Suppose there are just three possible hypotheses about the correct method of classification , and with posteriors 0.4, 0.3 and 0.3 respectively. Suppose given a new instance, x {\displaystyle x} , h 1 {\displaystyle h_{1}} classifies it as positive, whereas the other two classify it as negative.