True positive (TP): Difference between revisions

Latest revision as of 21:11, 17 March 2023

See also: Machine learning terms

Introduction

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

Binary classification refers to two possible outcomes of a prediction. For instance, in medical diagnosis scenarios, positive and negative classes 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. In many applications such as medical diagnosis or fraud detection, correctly recognizing positive instances is vital.

Explain Like I'm 5 (ELI5)

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.

Now, if someone throws the red ball to you and you catch it, that is an impressive accomplishment. It signifies that you did your duty by catching the red ball as intended.

Machine learning also has things that we want to "catch" or "detect," such as images of cats or dogs. A true positive in machine learning means the computer program correctly identified an image as being a cat (or whatever we were trying to detect).

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.

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 {{see also|Machine learning terms}}
 ==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==
 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)==
-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.
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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.
+[[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]
-[[Category:Terms]] [[Category:Machine learning terms]]