Which concept in data analysis does a taxi fare system that varies by both distance and time closely resemble?

The concept that closely resembles a taxi fare system varying by both distance and time is regression analysis. This is because regression analysis is a statistical method used to examine the relationship between dependent and independent variables. In this context, the fare charged by a taxi can be thought of as the dependent variable, which depends on independent variables such as distance traveled and time of day.

By using regression analysis, a model can be developed that quantifies how changes in distance and time influence the taxi fare. This may involve creating a mathematical formula or algorithm that predicts fares based on the input variables. It allows for a more nuanced understanding of fare pricing, potentially incorporating additional factors like surge pricing or discounts based on time of day or location.

In contrast, unstructured data extraction focuses on data that does not have a predefined data model or organization, which is not relevant to the fare system directly. Data visualization with R involves creating graphical representations of data, which would be a means of displaying analysis results rather than deriving them. The nearest neighbor algorithm relates to classification or regression based on the proximity of data points, which does not directly apply to calculating fares based on distance and time.