The more attributes there are,the easier it is to build a model that fits the sample data,but that is worthless as a predictor.Which of the following best explains this phenomenon?
A)the free rider problem
B)the curse of dimensionality
C)the tragedy of the commons
D)the zero-sum game

Question

Quizplus · Accepted Answer

The curse of dimensionality refers to the phenomenon where the predictive power of a model decreases as the number of variables (dimensions) increases. This is because the sample size needed to accurately represent the data grows exponentially with the number of dimensions, making it difficult to build a reliable model with a small sample size. As a result, models that fit the sample data well become less useful as predictors when too many attributes are included.

The More Attributes There Are,the Easier It Is to Build