Variable reduction is a crucial step for accelerating model building without losing the potential predictive power of the data. With the advent of Big Data and sophisticated data mining techniques, the number of variables encountered is often tremendous making variable selection or dimension reduction techniques imperative to produce models with acceptable accuracy and generalization. The temptation to build an ecological model using all available information (i.e., all variables) is hard to…Continue
Added by Valiance Solutions on April 21, 2017 at 9:20pm — No Comments
In this article, we will discuss the so called 'Curse of Dimensionality', and explain why it is important when designing a classifier. In the following sections I will provide an intuitive explanation of this concept, illustrated by a clear example of overfitting due to the curse of dimensionality.
Consider an example in which we have a set of images, each of which depicts either a cat or a dog. We would like to create a classifier that is able to…Continue