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 how they process and learn from data.

The goal of supervised learning is to build a predictive model that can make accurate predictions or classifications based on labeled data. In supervised learning, the model is trained using a dataset where the inputs (features) are paired with the correct outputs (labels). The model learns from this data and generalizes patterns, so it can accurately predict the output for new, unseen data.

Key Goals of Supervised Learning:

1. Prediction: The primary objective is to make predictions for future data based on historical labeled data. For example:

   • Regression: Predicting a continuous value (e.g., house prices based on features like size, location, etc.).

   • Classification: Categorizing data into predefined classes (e.g., classifying emails as spam or not spam).

2. Pattern Recognition: The model identifies underlying patterns or relationships between the input features and the output labels. This allows the model to generalize to new, unseen examples.

3. Generalization: The goal is for the model to not only perform well on the training data but also to generalize well to new data it hasn't seen before, ensuring it works effectively in real-world scenarios.

Steps in Supervised Learning:

• Training: The model is trained on a labeled dataset where the true output is known.

• Learning: The model uses algorithms (such as linear regression, decision trees, or neural networks) to learn the relationship between input features and the target output.

• Prediction/Testing: After training, the model is tested on a separate dataset (often called a test set) to evaluate its accuracy and performance.

In summary, the goal of supervised learning is to create a model that can accurately predict outcomes based on historical data with known labels, helping in decision-making, forecasting, and automation.

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