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Binary classification: Difference between revisions
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{{see also|Machine learning terms}} | |||
Binary classification is a type of machine learning problem in which the goal is to classify input data into two classes or categories. Often, these classes are labeled as positive (1) and negative(0), or true(1) and false(0) respectively. | Binary classification is a type of machine learning problem in which the goal is to classify input data into two classes or categories. Often, these classes are labeled as positive (1) and negative(0), or true(1) and false(0) respectively. | ||
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Examples of binary classification problems include spam email detection, fraud detection, disease diagnosis and sentiment analysis. | Examples of binary classification problems include spam email detection, fraud detection, disease diagnosis and sentiment analysis. | ||
[[Category:Terms]] [[Category:Machine learning terms]] | |||