Which algorithm is typically used for predicting continuous numerical values?

Linear regression is typically used for predicting continuous numerical values due to its foundational basis in estimating the relationship between a dependent variable and one or more independent variables. This algorithm operates by fitting a linear equation to observed data points, allowing for predictions of outcomes based on linear relationships.

In the context of regression analysis, the goal is to find the best-fitting line through the data points, expressed mathematically as ( Y = aX + b ), where ( Y ) is the predicted value, ( X ) represents the independent variable(s), ( a ) denotes the slope of the line, and ( b ) is the y-intercept. Linear regression is particularly effective in scenarios where this linear relationship holds true, making it a robust choice for continuous value prediction.

On the other hand, decision trees are more commonly employed for classification tasks, providing categorical outcomes, although they can also be adapted for regression problems. K-means clustering is primarily used for unsupervised learning and is focused on grouping data points into clusters rather than predicting numerical values. Support vector machines are versatile and can be adapted for both classification and regression tasks (as in Support Vector Regression), but linear regression remains the most straightforward option for predicting continuous values effectively.