Regression in Supervised Learning
Regression is a supervised learning algorithm that predicts a continuous numerical outcome based on input features. Imagine you want to predict house prices, and you have data on factors like square footage, number of bedrooms, and location. A fundamental example is Linear Regression. Imagine plotting data points on a graph where one axis represents the input, and the other represents the output. Linear Regression could predict house prices based on factors like square footage. The algorithm learns the relationship between input features and output values, enabling it to make accurate predictions for unseen data, making it a powerful tool for various applications.
A beginner’s guide to supervised learning with Python
Supervised learning is a foundational concept, and Python provides a robust ecosystem to explore and implement these powerful algorithms. Explore the fundamentals of supervised learning with Python in this beginner’s guide. Learn the basics, build your first model, and dive into the world of predictive analytics.
Table of Content
- What is Machine Learning?
- What is supervised learning in ML
- Types of Supervised Learning
- Classifications in Supervised learning
- Regression in Supervised Learning
- Supervised Machine Learning Algorithm
- Conclusion
- Frequently Asked Question (FAQs)