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==
-[[True positive (TP)]] is when the [[machine learning model]] correctly predicts the [[positive class]]. [[Machine learning]] [[classification]] is the process of classifying [[data]] into distinct [[classes]]. When assessing a [[model]]'s performance, it is essential to assess its capacity for correctly predicting each data instance's [[class]]. One important evaluation [[metric]] used in [[binary classification]] is [[True Positive (TP)]], which is when positive [[examples]] is correctly classified as positive by the model.
+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==
-True Positive is when binary classification models correctly predict a positive class. Consider a binary classification problem where we need to predict if someone has or does not have a disease based on some medical tests. In this scenario, the positive class represents those with the disease, while the negative class represents those without. If our model correctly predicts someone has the disease, and they actually do, then this prediction is known as a True Positive.
+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. In many applications such as medical diagnosis or fraud detection, correctly recognizing positive instances is vital.
 ==Explain Like I'm 5 (ELI5)==
-Machine learning algorithms strive for true positives - like when you find the toy you were searching for.
+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.
-Imagine playing a game where you must locate a hidden toy in your room. When you finally locate it, you are thrilled that you found what you were searching for - an affirmative moment as you have successfully located what you needed. This is an achievement in itself as proof that your efforts paid off!
+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).
-Machine learning teaches computers how to recognize objects, like pictures of cats. A true positive is when the computer correctly determines that something is indeed a cat when it really is one.
+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.
-Similar to when humans play "find the cat", wherein a computer can correctly identify when something in an image is indeed a cat. This is beneficial, as we want the machine to be able to correctly recognize when something is indeed a cat.
+[[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]