FeatureAuswahl
FeatureAuswahl is a German term that translates to "feature selection" in English. It is a crucial process in machine learning and data mining, aimed at identifying and selecting a subset of relevant features from a larger set of input variables. The primary goal of feature selection is to improve model performance, reduce computational costs, and enhance the interpretability of the resulting model.
There are several reasons why feature selection is important. Firstly, high-dimensional datasets, containing a large number
Feature selection methods can be broadly categorized into three types: filter methods, wrapper methods, and embedded