Regression
Regression is a supervised learning technique used to predict continuous numerical values from input data. Unlike classification, which assigns categories, regression outputs real-valued predictions such as prices, probabilities, or measurements.
Background
Originating in statistics in the 19th century, regression has evolved into a cornerstone of modern machine learning. It is widely used to model relationships between independent variables (features) and a dependent variable (target), making it one of the most interpretable predictive methods.
Examples
- Real estate: predicting housing prices based on features like size, location, and number of rooms.
- Economics & finance: forecasting sales, market demand, or stock trends.
- Medicine: estimating blood pressure levels from patient data.
- Energy: predicting electricity consumption from past usage patterns.
Strengths and challenges
- ✅ Easy to implement and interpret.
- ✅ Useful baseline for many prediction problems.
- ❌ Struggles with highly non-linear or complex relationships.
- ❌ Sensitive to noise and outliers.
Regression is not confined to simple linear models. Variants such as polynomial regression, logistic regression for binary outcomes, and regularized approaches like ridge and lasso regression help address issues of overfitting and multicollinearity, making them suitable for real-world noisy data.
In modern machine learning, regression extends into more advanced methods, including Random Forest Regression, Support Vector Regression (SVR), and deep neural networks. These models capture complex nonlinear patterns and interactions between variables, enabling high predictive accuracy in fields such as climate modeling or financial forecasting.
Evaluation plays a central role in regression practice. Beyond training fit, metrics like RMSE, MAE, and the coefficient of determination (R²) are essential to assess how well the model generalizes to unseen data. Proper cross-validation is often necessary to detect and prevent overfitting.
Regression also maintains its importance in scenarios where interpretability matters as much as prediction. Being able to explain how input variables affect the outcome remains critical in domains like healthcare, economics, and policy-making, where decisions must be justified and transparent.
📚 Further Reading
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning.
- Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning.
