Probabilistic Classification
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May 15, 2017, at 05:50 PM
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In ''[[https://en.wikipedia.org/wiki/Probabilistic_classification | probabilistic classification]]'' problems, the label space $\mathbf{Y}$ is finite and [[Forecaster]] is required to output a probability measure on $\mathbf{Y}$. In ''binary classification problems'', the size of $\mathbf{Y}$ is 2; usually, $\mathbf{Y}=\{0,1\}$ or $\mathbf{Y}=\{-1,1\}$, and a forecast can be interpreted as a number in the interval ${[0,1]}$ (the predicted probability of the outcome 1).
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In ''[[https://en.wikipedia.org/wiki/Probabilistic_classification | probabilistic classification]]'' problems, the label space $\mathbf{Y}$ is finite and [[Forecaster]] is required to output a probability measure on $\mathbf{Y}$. In ''binary classification problems'', the size of $\mathbf{Y}$ is 2; usually, $\mathbf{Y}=\{0,1\}$ or $\mathbf{Y}=\{-1,1\}$, and a forecast can be interpreted as a number in the interval ${[0,1]}$ (the predicted probability of the outcome 1).
An example of forecasting strategies for probabilistic classification is various [[Venn predictor]]s.
An example of forecasting strategies for probabilistic classification is various [[Venn predictor]]s.
May 15, 2017, at 05:44 PM
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In ''[[https://en.wikipedia.org/wiki/Probabilistic_classification | probabilistic classification]]'' problems, the label space $\mathbf{Y}$ is finite and [[Forecaster]] is required to output a probability measure on $\mathbf{Y}$. In ''binary classification problems'', the size of $\mathbf{Y}$ is 2; usually, $\mathbf{Y}=\{0,1\}$ or $\mathbf{Y}=\{-1,1\}$, and a forecast can be interpreted as a number in the interval ${[0,1]}$ (the predicted probability of the outcome 1).