Supervised Machine Learning

Supervised Machine Learning

Supervised Machine Learning is a type of Machine Learning where an algorithm is trained on a labeled dataset. Each training example is composed of an input object (features) and an output value (label). The algorithm learns to map inputs to outputs by analyzing the provided data and making predictions on unseen data based on this training.

Examples

• Classification: Identifying whether an email is spam or not based on labeled examples of spam and non-spam emails.
• Regression: Predicting house prices based on features like size, location, and number of bedrooms using historical sales data.

Use Cases

• Healthcare: Diagnosing diseases based on patient symptoms and historical medical records.
• Finance: Credit scoring by predicting the likelihood of a borrower defaulting based on past loan performance.
• Retail: Customer segmentation based on purchasing behavior to improve targeted Marketing efforts.