Dimensionality

“Dimensionality” refers to the number of attributes or features that represent a data point in a dataset. In a simple sense, it’s like the number of different characteristics or dimensions we have information on. For example, if you are collecting data on houses, the dimensions might include the number of bedrooms, square footage, price, etc. Each of these is a dimension of your data.

In high-dimensional spaces, such as those often found in machine learning, each data point can have hundreds, thousands, or even millions of dimensions. High dimensionality can make data analysis more challenging, a phenomenon known as the “curse of dimensionality,” which can lead to issues like overfitting and increased computational complexity. Reducing dimensionality through techniques like principal component analysis (PCA) or embeddings can help alleviate these problems by distilling the most informative features into a lower-dimensional space.