Definition:
Supervised Learning /ˈsuː.pə.vaɪzd ˈlɜː.nɪŋ/ noun — In machine learning, supervised learning is a method where a model is trained on a labeled dataset, meaning each input example is paired with a correct output or target. The model learns to map inputs to outputs by minimizing the difference between its predictions and the known labels.
The goal of supervised learning is to generalize—to make accurate predictions on new, unseen data based on the patterns learned during training.
Common tasks include:
- Classification (e.g., spam detection, image labeling)
- Regression (e.g., predicting prices, temperatures)
Key components:
- Training data: consists of input–output pairs
- Loss function: measures prediction error
- Optimization algorithm: adjusts model parameters to minimize the loss
Popular supervised algorithms include:
- Linear regression
- Logistic regression
- Support Vector Machines (SVMs)
- Decision Trees
- Neural Networks
Supervised learning is widely used in applications such as fraud detection, medical diagnostics, speech recognition, and recommendation systems, offering high performance where quality labeled data is available.
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