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Supervised Learning

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Supervised learning is a type of machine learning where a model is trained on a labeled dataset. This means each training example includes both the input data and the correct output (label). The model learns to map inputs to outputs so it can predict the output for new, unseen data.

Key Points:

  • Input & Output: The training data includes inputs (features) and known outputs (labels).
  • Goal: Learn a function that maps inputs to the correct output.
  • Common Algorithms: Linear regression, logistic regression, decision trees, support vector machines (SVM), and neural networks.
  • Example Use Cases:
    • Email spam detection (spam or not spam)
    • Image classification (cat, dog, etc.)
    • Sentiment analysis (positive, neutral, negative)

Would you like a simple example or a visual to go with it?