What is the curse of dimensionality?

The danger of dimensionality is a phenomenon that occurs in a variety of fields like machine learning, statistics, and optimization when working with data that is high-dimensional. It refers to the problems and issues that are triggered when the amount of variables or dimensions within the data increases. The concept was invented in the 1950s by Richard Bellman in his work on dynamic programming and has since evolved into an important concept for understanding the limitations and complexity of high-dimensional data. https://www.sevenmentor.com/da....ta-science-course-in

In simple terms, as the size of the dimensions increases the volume of data required to fill the area adequately grows exponentially. This rapid growth in the volume of data poses serious issues in terms of the complexity of computation as well as data sparsity and the general effectiveness of algorithms. Understanding the dangers of dimensionality is vital for data scientists, researchers as well as analysts working on datasets that have a myriad of characteristics.