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Classification threshold: Difference between revisions
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{{see also|Machine learning terms}} | |||
==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== | ==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]]. | In [[binary classification]], the 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: | A logistic regression model produces a raw value of between 0 to 1. Then: | ||
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==What is Classification Threshold?== | ==What is Classification Threshold?== | ||
The classification threshold is a value that indicates the minimum probability that data belonging to one class should be included within that group. It can be set as either a fixed value or dynamically adjusted based on data characteristics. | |||
The classification threshold is critical in determining the [[precision]] and [[recall]] of a [[classification model]]. Precision refers to the proportion of predicted positive cases that turn out to be true, while recall measures how accurately actual positive cases are identified as such by the model. When choosing a threshold value for classification, keep in mind that both precision and recall may be compromised; often there is an inherent trade-off between them. | The classification threshold is critical in determining the [[precision]] and [[recall]] of a [[classification model]]. Precision refers to the proportion of predicted positive cases that turn out to be true, while recall measures how accurately actual positive cases are identified as such by the model. When choosing a threshold value for classification, keep in mind that both precision and recall may be compromised; often there is an inherent trade-off between them. | ||
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===Threshold based on Precision-Recall Curve=== | ===Threshold based on Precision-Recall Curve=== | ||
The [[precision-recall curve]] is a graphical illustration of the tradeoff between precision and recall. By using this curve, one can select an optimal threshold value based on | The [[precision-recall curve]] is a graphical illustration of the tradeoff between precision and recall. By using this curve, one can select an optimal threshold value based on the model's performance. | ||
===Dynamic Threshold Strategy=== | ===Dynamic Threshold Strategy=== | ||
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==Explain Like I'm 5 (ELI5)== | ==Explain Like I'm 5 (ELI5)== | ||
When we ask the computer to examine a picture and tell us whether it's of a dog or cat, it gives us an approximate number that indicates how likely it is either one. If the number is high, then the computer thinks it's probably a dog; if low, then it thinks it might be cat. | When we ask the computer to examine a picture and tell us whether it's of a dog or a cat, it gives us an approximate number that indicates how likely it is either one is. If the number is high, then the computer thinks it's probably a dog; if low, then it thinks it might be a cat. | ||
Sometimes, however, the computer can't tell if a number is from a dog or cat and needs help deciding. That's where classification thresholds come into play; we can decide on an outcome by setting a number. | Sometimes, however, the computer can't tell if a number is from a dog or cat and needs help deciding. That's where classification thresholds come into play; we can decide on an outcome by setting a number. | ||
[[Category:Terms]] [[Category:Machine learning terms]] | |||