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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.
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.
The softmax function is used in various multiclass classification methods, such as multinomial logistic regression (also known as softmax regression), [2]: 206–209 [6] multiclass linear discriminant analysis, naive Bayes classifiers, and artificial neural networks. [7]
A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). [1] While it is one of several forms of causal notation, causal networks are special cases of Bayesian ...
A strong learner is a classifier that is arbitrarily well-correlated with the true classification. Robert Schapire answered the question in the affirmative in a paper published in 1990. [ 5 ] This has had significant ramifications in machine learning and statistics , most notably leading to the development of boosting.
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
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 computer science and statistics, Bayesian classifier may refer to: any classifier based on Bayesian probability; a Bayes classifier, one that always chooses the class of highest posterior probability in case this posterior distribution is modelled by assuming the observables are independent, it is a naive Bayes classifier