Probabilistic Classification Model
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A Probabilistic Classification Model is a classification model that can provide a probability estimate.
- Context:
- It can be produced by a Probabilistic Classification Modeling System, such as a Probabilistic Classification Model Calibration System.
- Example(s):
- Counter-Example(s):
- See: Statistical Classification, Probability Structure, Conditional Probability.
References
2014
- (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/probabilistic_classification Retrieved:2014-11-10.
- In machine learning, a probabilistic classifier is a classifier that is able to predict, given a sample input, a probability distribution over a set of classes, rather than only predicting a class for the sample. Probabilistic classifiers provide classification with a degree of certainty, which can be useful in its own right, or when combining classifiers into ensembles. Formally, a probabilistic classifier is a conditional distribution [math]\displaystyle{ \operatorname{P}(Y \vert X) }[/math] over a finite set of classes , given inputs . Deciding on the best class label [math]\displaystyle{ \hat{y} }[/math] for can then be done using the optimal decision rule :[math]\displaystyle{ \hat{y} = \operatorname{\arg\max}_{y} \operatorname{P}(Y=y \vert X) }[/math]
Binary probabilistic classifiers are also called binomial 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 naturally probabilistic. Other models such as support vector machines are not, but methods exist to turn them into probabilistic classifiers.
- In machine learning, a probabilistic classifier is a classifier that is able to predict, given a sample input, a probability distribution over a set of classes, rather than only predicting a class for the sample. Probabilistic classifiers provide classification with a degree of certainty, which can be useful in its own right, or when combining classifiers into ensembles. Formally, a probabilistic classifier is a conditional distribution [math]\displaystyle{ \operatorname{P}(Y \vert X) }[/math] over a finite set of classes , given inputs . Deciding on the best class label [math]\displaystyle{ \hat{y} }[/math] for can then be done using the optimal decision rule :[math]\displaystyle{ \hat{y} = \operatorname{\arg\max}_{y} \operatorname{P}(Y=y \vert X) }[/math]