Which classification method ensures that there are an equal number of features per class?

The quantile classification method is designed to create classes with an equal number of features in each, distributing the data points into groups based on their rank order. This method sorts the data and divides it into intervals, or quantiles, such that each class contains approximately the same number of observations.

For example, if you apply quantile classification to a dataset of 100 features, and you want to create 4 classes, each class will contain 25 features. This equal distribution makes quantiles particularly useful when the goal is to highlight relative rankings and ensure that every class represents a similar amount of data, allowing for balanced comparison between classes.

In contrast, the other methods focus on different criteria for classification. Defined interval uses fixed ranges that do not consider the distribution of data, while natural breaks involves identifying groupings in the data based on inherent gaps or clusters. Equal interval creates classes with the same numerical range, but it does not guarantee that an equal number of features will fall into each class.