Regression

Regression is a fundamental technique in machine learning and statistics that focuses on predicting continuous outcomes based on one or more input variables. Unlike classification, which assigns data to discrete categories, regression aims to model the relationship between inputs (features) and a continuous target variable, allowing for precise predictions of numerical values. This makes regression essential in fields like finance, healthcare, economics, and engineering, where accurate forecasts are crucial.

In regression tasks, the model learns from labeled data, identifying patterns and correlations within the dataset to generate predictions on new data points. Common applications of regression include predicting housing prices, stock market trends, and patient health metrics. The strength of regression lies in its versatility; it can model simple linear relationships as well as complex, non-linear patterns using various techniques.

Throughout this chapter, we’ll explore different types of regression algorithms, including Linear Regression, Polynomial Regression, Support Vector Regression (SVR), and Regularized Regression Techniques like Lasso and Ridge Regression. Each technique has its unique strengths and applications, offering diverse tools to accurately predict and understand numerical data in a range of scenarios.