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)]] | 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)== | ||
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]] | ||