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 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 challenge of building a predictive model when there are too many attributes or features in the data. This can lead to overfitting, where the model fits the sample data too closely and is not able to generalize to new, unseen data. As the number of attributes increases, the amount of data required to build an accurate model also increases, making it more difficult to find meaningful patterns and relationships within the data.

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