Summary
Supervised learning is a prevalent form of machine learning where the algorithm learns from labeled data. It involves predicting outcomes based on known input-output pairs. For example, supervised learning can be used to classify emails as spam or not spam, predict student exam outcomes, or detect fraudulent credit card transactions by analyzing specific features. It is a powerful tool for making predictions and decisions based on existing data patterns.
Supervised Machine Learning
Supervised learning is a common type of machine learning where the algorithm is trained on a labeled dataset. The output variable is already known for each input variable.
Classification of Emails
In supervised learning, emails can be classified into spam or not spam categories based on features like keywords, exclamation marks, and patterns.
Predicting Student Performance
Supervised learning can predict whether a student will pass or fail an exam based on features like hours of study and sleep.
Fraud Detection in Credit Card Transactions
Supervised learning can identify fraudulent credit card transactions by analyzing features such as transaction amount, merchant category, location, and time.
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