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Best Machine learning model class of All Time is a public top list created by Listnerd on rankly.com on November 27th 2012. Items on the Best Machine learning model class of All Time top list are added by the rankly.com community and ranked using our secret ranking sauce. Best Machine learning model class of All Time has gotten 175 views and has gathered 5 votes from 5 voters. O O

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    Generative model

    In probability and statistics, a generative model is a model for randomly generating observable data, typically given some hidden parameters. It specifies a joint probability distribution over observation and label sequences. Generative models are used in machine learning for either modeling data directly (i.e., modeling observed draws from a probability density function), or as an intermediate step to forming a conditional probability density function. A conditional distribution can be formed from a generative model through the use of Bayes' rule. Shannon (1948) gives an example in which a table of frequencies of English word pairs is used to generate a sentence beginning with "representing and speedily is an good"; which is not proper English but which will increasingly approximate it as the table is moved from word pairs to word triplets etc. Generative models contrast with discriminative models, in that a generative model is a full probabilistic model of all variables, whereas a discriminative model provides a model only for the target variable(s) conditional on the observed variables. Thus a generative model can be used, for example, to simulate (i.e. generate) values of any
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    Discriminative model

    Discriminative models are a class of models used in machine learning for modeling the dependence of an unobserved variable on an observed variable . Within a statistical framework, this is done by modeling the conditional probability distribution , which can be used for predicting from . Discriminative models differ from generative models in that they do not allow one to generate samples from the joint distribution of and . However, for tasks such as classification and regression that do not require the joint distribution, discriminative models can yield superior performance. On the other hand, generative models are typically more flexible than discriminative models in expressing dependencies in complex learning tasks. In addition, most discriminative models are inherently supervised and cannot easily be extended to unsupervised learning. Application specific details ultimately dictate the suitability of selecting a discriminative versus generative model. Examples of discriminative models used in machine learning include:
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