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Binary classification: Difference between revisions
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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 spam or non-spam. The input data consists of the content of the email, and the output is either "spam" or "not spam." | |||
#Credit risk assessment: The goal is to classify loan applicants as "high-risk" or "low-risk" based on their credit history and other factors. The input data consists of various features such as income, credit score, and employment history, and the output is either "high-risk" or "low-risk." | |||
#Medical diagnosis: The goal is to classify patients as having a particular disease or not based on their symptoms and medical history. The input data consists of various features such as age, gender, blood pressure, and medical test results, and the output is either "positive" or "negative." | |||
#Fraud detection: The goal is to classify financial transactions as fraudulent or non-fraudulent. The input data consists of various features such as the amount of the transaction, the location of the transaction, and the type of transaction, and the output is either "fraudulent" or "non-fraudulent." | |||
==How is Binary Classification Accomplished?== | ==How is Binary Classification Accomplished?== | ||