MerkmalsEngineering

Merkmalsengineering, also known as feature engineering, is a crucial process in machine learning that involves using domain knowledge to extract features from raw data. These features are then used to train machine learning models. The goal of feature engineering is to create features that are representative of the underlying problem and help the model to learn effectively. This can involve transforming existing variables, creating new variables by combining existing ones, or selecting the most relevant features from a larger set.

The process typically begins with understanding the data and the problem at hand. This often involves exploratory data analysis to identify patterns, relationships, and potential issues within the data. Based on this understanding, various techniques can be applied. Common transformations include scaling numerical data (e.g., min-max scaling, standardization), encoding categorical variables (e.g., one-hot encoding, label encoding), and handling missing values.

Creating new features can involve techniques like polynomial features, interaction terms, or aggregation. For instance, in a time-series dataset, new features like moving averages or lag features can be created. Domain knowledge plays a significant role here, as experts can suggest features that are known to be predictive.

Feature selection is another important aspect, where irrelevant or redundant features are removed to improve model performance and reduce complexity. Techniques for feature selection include filter methods, wrapper methods, and embedded methods. Merkmalsengineering is often an iterative process, where different feature sets are tried and evaluated to find the optimal combination for a given machine learning task.