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True positive (TP): Difference between revisions

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{{see also|Machine learning terms}}
{{see also|Machine learning terms}}
==Introduction==
==Introduction==
In [[machine learning]], [[true positive (TP)]] refers to [[example]]s that are correctly predicted as positive by a [[classification model]]. More precisely, true positives refer to examples that actually exist as positives in the [[test data set]] and were classified correctly by the [[model]] as such. True positives represent one of four possible outcomes from [[binary classification]] tasks - other options are [[true negative]], [[false positive]], and [[false negative]].
In [[machine learning]], a [[true positive (TP)]] is an instance where the [[model]] correctly recognizes an instance of a [[class]]. In [[binary classification]], true positive is when the model correctly predicts the [[positive class]].
 
Consider a model trained to recognize pictures of cats. If it correctly recognizes such a picture as featuring a cat, this would be considered a true positive; meaning the model has correctly recognized the presence of an identifiable feature or characteristic (in this case, an image of a cat) within input data.


==Example==
==Example==
Binary classification refers to two possible outcomes of a prediction. For instance, in medical diagnosis scenarios, [[positive class|positive]] and [[negative class]]es represent patients with and without certain diseases, respectively. A classification model attempts to predict whether a newly enrolled patient has this disease or not by analyzing certain [[features]] or attributes about them. Its prediction can either be positive or negative depending on whether it believes they already have it or not.
Binary classification refers to two possible outcomes of a prediction. For instance, in medical diagnosis scenarios, [[positive class|positive]] and [[negative class]]es represent patients with and without certain diseases, respectively. A classification model attempts to predict whether a newly enrolled patient has this disease or not by analyzing certain [[features]] or attributes about them. Its prediction can either be positive or negative depending on whether it believes they already have it or not.


True positive occurs when a model accurately predicts a positive outcome for a patient with the disease. True positive is essential as it demonstrates the model's capability to accurately detect these instances in real-world applications.
True positive occurs when a model accurately predicts a positive outcome for a patient with the disease. True positive is essential as it demonstrates the model's capability to accurately detect these instances in real-world applications. In many applications such as medical diagnosis or fraud detection, correctly recognizing positive instances is vital.


==Explain Like I'm 5 (ELI5)==
==Explain Like I'm 5 (ELI5)==
Let me define a true positive (TP) in an easy-to-understand terms even for children of five.
Imagine you're playing a game of catch with two balls: red and blue. Your objective is to catch the red ball every time it's thrown to you.
Imagine you're playing a game of catch with two balls: red and blue. Your objective is to catch the red ball every time it's thrown to you.


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Just as our game of catch the red ball was a true positive, correctly identifying an image as a cat using machine learning is also a real success.
Just as our game of catch the red ball was a true positive, correctly identifying an image as a cat using machine learning is also a real success.


[[Category:Terms]] [[Category:Machine learning terms]]
[[Category:Terms]] [[Category:Machine learning terms]]