Supervised Learning

This is a placeholder page for Supervised Learning concepts. You can replace this with your HTML content.

In supervised learning, algorithms are trained using labeled data, where the input data is paired with the correct output. The goal is for the algorithm to learn a mapping function that can predict the output for new, unseen input data.

Key Characteristics:

• Input data is labeled.
• Used for prediction tasks.
• Common tasks include Classification and Regression.

Examples:

• Classification: Email spam detection, image recognition (e.g., identifying cats vs. dogs).
• Regression: Predicting house prices, forecasting stock market trends.

Feel free to expand this page with more detailed explanations, examples, and specific algorithms like Linear Regression, Logistic Regression, Decision Trees, SVMs, etc.