What is the goal of supervised learning?

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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in  The primary goal of a data science project is to extract actionable insights from data to support better decision-making, predictions, or automation—ultimately solving a specific business or real-world problem. 

The goal of supervised learning is to train a machine learning model to learn patterns and relationships between input data (features) and the correct output (labels) so that it can accurately predict outcomes for new, unseen data.

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Key Objectives:

1. Learn from labeled data

   • The model is provided with training data that includes both inputs and their correct outputs.

   • Example: Feeding a model emails labeled as “spam” or “not spam.”

2. Make accurate predictions

   • Once trained, the model should correctly predict labels for new inputs.

   • Example: Predicting house prices based on size, location, and features.

3. Generalize well

   • The model should not just memorize training data but also perform well on real-world, unseen data.

4. Support decision-making

   • Helps in automation and better business decisions by providing reliable predictions.

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✅ In short: The goal of supervised learning is to map inputs to outputs using labeled data so the model can predict future outcomes with high accuracy.

Read More

What is the difference between supervised and unsupervised learning?

What does unsupervised learning do?

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