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discriminative

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The answer DISCRIMINATIVE has 0 possible clue(s) in existing crosswords.

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Definitions of discriminative in various dictionaries:

adj - capable of making fine distinctions

adj - expressing careful judgment

Drawing distinctions.

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Possible Dictionary Clues
Drawing distinctions.
Marked by or showing prejudice: discriminative hiring practices.
expressing careful judgment
capable of making fine distinctions

Discriminative might refer to
Discriminative models, also called conditional models, are a class of models used in machine learning for modeling the dependence of unobserved (target) variables * * * * y * * * {\displaystyle y} * on observed variables * * * * x * * * {\displaystyle x} * . Within a probabilistic framework, this is done by modeling the conditional probability distribution * * * * P * ( * y * * \| * * x * ) * * * {\displaystyle P(y\|x)} * , which can be used for predicting * * * * y * * * {\displaystyle y} * from * * * * x * * * {\displaystyle x} * . * Discriminative models, as opposed to generative models, do not allow one to generate samples from the joint distribution of observed and target variables. However, for tasks such as classification and regression that do not require the joint distribution, discriminative models can yield superior performance (in part because they have fewer variables to compute). 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 support unsupervised learning. Application-specific details ultimately dictate the suitability of selecting a discriminative versus generative model.

Discriminative might refer to

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* Discriminative models, as opposed to generative models, do not allow one to generate samples from the joint distribution of observed and target variables. However, for tasks such as classification and regression that do not require the joint distribution, discriminative models can yield superior performance (in part because they have fewer variables to compute). 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 support unsupervised learning. Application-specific details ultimately dictate the suitability of selecting a discriminative versus generative model.

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