Kennzahlenreduktion

Kennzahlenreduktion, also known as feature selection or dimensionality reduction, is a process used in machine learning and data analysis to reduce the number of input variables or features in a dataset. The primary goal is to select a subset of relevant features that can be used to train a model, thereby improving model performance, reducing computational costs, and simplifying model interpretation.

There are several motivations behind feature selection. Firstly, high-dimensional datasets, those with a large number of features, can lead to the "curse of dimensionality," where the data becomes sparse and models struggle to generalize. By removing irrelevant or redundant features, Kennzahlenreduktion can mitigate this problem. Secondly, fewer features mean less data to process, leading to faster training times and lower memory requirements for models. Thirdly, models with fewer features are often easier to understand and interpret, making it simpler to identify the key drivers of a phenomenon.

Common methods for Kennzahlenreduktion can be broadly categorized into three types: filter methods, wrapper methods, and embedded methods. Filter methods select features based on their statistical properties, independent of any machine learning model. Wrapper methods use a specific machine learning model to evaluate different subsets of features. Embedded methods perform feature selection as part of the model training process. The choice of method often depends on the dataset characteristics and the specific goals of the analysis.