What is Regression?
Regression is a supervised machine learning method that predicts continuous numerical values from input features.
In regression, a model learns the relationship between input variables and a target output that can take any real number value, such as price or temperature. The training process adjusts model parameters to minimize the difference between predicted and actual values, often using a loss function like mean squared error.
Common algorithms include linear regression, which fits a straight line, and more flexible methods like decision trees or neural networks. Key considerations involve checking assumptions, handling overfitting through regularization, and evaluating performance with metrics such as R-squared or mean absolute error.
Unlike classification, regression outputs are not limited to discrete categories, making it suitable for forecasting and trend analysis tasks.
Example
A real-estate app uses regression to estimate a house's selling price from features like square footage, number of bedrooms, and location, outputting a dollar amount rather than a category.
Why it matters
Regression powers everyday predictions in finance, healthcare, and demand forecasting, forming a core building block for many modern AI systems that need numeric outputs.
Frequently asked questions
Regression predicts continuous numbers while classification assigns data to discrete categories.
Related terms
Classification is a supervised machine learning task that assigns input data to one of several predefined categories or classes based on patterns learned from labeled training examples.
Supervised learning is a machine learning method where a model is trained on data that already has correct answers attached, so it can learn to predict those answers for new data.
Overfitting happens when a machine learning model learns the training data too closely, including its noise and quirks, so it fails to perform well on new, unseen data.
Adam (Adaptive Moment Estimation) is a popular optimization algorithm used to train machine learning models by iteratively updating parameters based on gradients.
Clustering is an unsupervised machine learning technique that automatically groups similar data points together into clusters based on their features, without using any labeled examples.
Gradient descent is an optimization algorithm that finds the minimum of a function by repeatedly moving in the direction of the steepest downward slope. In machine learning it is used to minimize a model's error by adjusting parameters step by step.