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Answers Explanation & Hints:
Supervised learning is a type of machine learning where the model is trained on a labeled dataset, i.e., a dataset with known inputs and outputs. In supervised learning, the algorithm learns to map inputs to outputs based on labeled training data. There are two main types of supervised learning algorithms:
- Classification: This type of algorithm is used when the output variable is a category or label, such as “spam” or “not spam” for an email filtering system, or “fraudulent” or “not fraudulent” for a credit card fraud detection system. Classification algorithms learn to predict the category or label of a new input based on the patterns observed in the labeled training data.
- Regression: This type of algorithm is used when the output variable is a continuous value, such as the price of a house or the temperature of a room. Regression algorithms learn to predict the value of a new input based on the patterns observed in the labeled training data.
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