Classification threshold: Difference between revisions

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==Introduction==
==Introduction==
In [[machine learning]], [[classification]] is a task where the goal is to assign an [[input]] [[data point]] to one of several predefined categories or [[classes]]. One critical decision that must be made while performing classification is setting the [[classification threshold]]; this determines when the [[algorithm]] assigns a data point to one [[class]] or another.
In [[machine learning]], [[classification]] is a task where the goal is to assign an [[input]] [[data point]] to one of several predefined categories or [[classes]]. One critical decision that must be made while performing classification is setting the [[classification threshold]]; this determines when the [[algorithm]] assigns a data point to one [[class]] or another.
==Classification Threshold in Binary Classification==
In [[binary classification]], classification threshold converts the [[logistic regression model]]'s raw output into a prediction of the positive or negative class. The classification threshold is set by a person, and not by the model during [[training]].
A logistic regression model produces a raw value of between 0 to 1. Then:
*If the classification threshold is higher than the raw value, the positive class will be predicted.
*If the classification threshold is lower than the raw value, the negative class will be predicted.
Let's say, for example, that the threshold for classification is 0.7. The model will predict the positive class if the raw value exceeds 0.7. If the raw value is below 0.7, the model will predict the negative class.
The classification threshold choice strongly impacts the number of [[false positive]]s as well as [[false negative]]s.


==What is Classification Threshold?==
==What is Classification Threshold?==