Curse of dimensionality refers to the non-intuitive email list properties of the data observed when working in a higher dimensional space *. In particular, it is related to the ease of use and interpretation of distance and volume. This is one of my favorite topics in machine learning and statistics. This is because there are a wide range of applications (not unique to any machine learning method), very counter-intuitive and therefore awe-inspiring, and profound applications in every analytical method. It has a scary name that is "Cool" like an egyptian curse! Consider the following example for a quick grasp.

For example, suppose you drop a coin on a 100-meter line. How do you find it? It's simple, just walk the line and search. But what about 100x100 square meters? Field? Trying to find a coin on the soccer field (roughly) is already difficult. But what if you have a space email list of 100 x 100 x 100 cu.M? !! As you know, the height of the soccer field is 30 stories. Find coins there and do your best! In other words, it is essentially a "Curse of dimensionality". Many ml methods use distance measurement most segmentation and clustering techniques rely on calculating the distance between observations.

The well-known k-means segmentation assigns email list points to the nearest center. Distance metrics were also required for dbscan and hierarchical clustering. Distribution and density-based outlier detection algorithms also use relative distances to other distances to mark outliers. Supervised classification solutions such as k-nearest neighbors also use the distance between observations to assign classes to unknown observations. The support vector machine method transforms the observations around the selected kernel based on the distance between the observations and the kernel. Common forms of recommender systems include distance-based similarities between user and item attribute vectors.