What is the main focus of regression analysis in the context of machine learning and statistics?

Regression analysis is primarily concerned with predicting continuous outcomes by modeling the relationship between a dependent variable and one or more independent variables. In machine learning and statistics, this method allows practitioners to understand how changes in predictor variables (independent variables) impact a continuous response variable (dependent variable).

For example, in a scenario where you want to predict house prices based on factors like size, location, and number of bedrooms, regression analysis would enable you to determine how each of these factors influences the price, thus producing a continuous prediction. This capability is fundamental in applications ranging from finance to health care, where understanding and forecasting numerical values is essential.

While identifying trends and classifying data points are valuable aspects of data analysis, they do not align with the primary purpose of regression analysis. In contrast, classification focuses on categorizing data into discrete labels, and clustering pertains to grouping similar data points without any target value, both of which are distinct from the continuous outcome prediction that regression is designed for.