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Binary classification: Difference between revisions
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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | ==Introduction== | ||
[[Binary classification]] is a type of [[machine learning]] problem where the goal is to classify [[input]] [[data]] into one of two [[ | [[Binary classification]] is a type of [[machine learning]] problem where the goal is to classify [[input]] [[data]] into one of two [[class]]es or categories. These classes may be labeled positive (1) and negative (0), or true(1) and false(0) respectively. The classes are mutually exclusive. | ||
==What is Binary Classification?== | ==What is Binary Classification?== | ||
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Binary classification is a [[supervised learning]] task, meaning the [[algorithm]] is trained using labeled data where each data point has been assigned a label indicating its class membership. | Binary classification is a [[supervised learning]] task, meaning the [[algorithm]] is trained using labeled data where each data point has been assigned a label indicating its class membership. | ||
===Example Usecases=== | |||
#Email spam filtering: The goal is to classify incoming emails as either spam or not spam. Using the content of the email as input data, this system outputs either "spam" or "not spam." | |||
#Credit Risk Assessment: The aim is to classify loan applicants as either "high-risk" or "low-risk" based on their credit history and other factors. Input data includes features like income, credit score, and employment history; then an output labeled either "high-risk" or "low-risk." | |||
#Medical Diagnosis: This task aims to classify patients as either having or not a certain disease based on their symptoms and medical history. The input data consists of features such as age, gender, blood pressure, and medical test results; the output can then be classified into either "positive" or "negative." | |||
#Fraud Detection: The goal is to classify financial transactions as fraudulent or non-fraudulent based on features such as the amount, location, and type of transaction. Based on these input data points, an output labeled "fraudulent" or "non-fraudulent" is generated. | |||
==How is Binary Classification Accomplished?== | ==How is Binary Classification Accomplished?== | ||
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[[Category:Terms]] [[Category:Machine learning terms]] | [[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]] | ||