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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.
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
Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths.. In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.
Binary probabilistic classifiers are also called binary regression models in statistics. In econometrics, probabilistic classification in general is called discrete choice. Some classification models, such as naive Bayes, logistic regression and multilayer perceptrons (when trained under an appropriate loss function) are
More examples illustrating the use of density estimates for exploratory and presentational purposes, including the important case of bivariate data. [ 7 ] Density estimation is also frequently used in anomaly detection or novelty detection : [ 8 ] if an observation lies in a very low-density region, it is likely to be an anomaly or a novelty.
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
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]