What is the curse of dimensionality?

The curse of dimensionality refers to the problem of high-dimensional data, where the number of features or dimensions is much larger than the number of observations. As the number of dimensions increases, the volume of the data space grows exponentially, which makes it increasingly difficult to find patterns or structure in the data. The curse of dimensionality can be addressed by using techniques such as feature selection, dimensionality reduction, and data preprocessing.