The more attributes there are in a sample data,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 tragedy of the commons
B)the curse of dimensionality
C)the zero-sum game
D)the free rider problem

Question

Quizplus · Accepted Answer

The curse of dimensionality refers to the problems that arise in trying to analyze or model data with a large number of dimensions, such as difficulty visualizing the data, computational complexity, and overfitting. With too many attributes, the model may become too complex and fit too closely to the sample data, but fail to generalize well to new data.

The More Attributes There Are in a Sample Data,the Easier