Binary classification: Difference between revisions

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 ===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."
+#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 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."
+#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: 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."
+#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. 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."
+#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?==