Modellreduktionsverfahren

Modellreduktion is a concept in statistical modeling and machine learning that refers to the process of simplifying a complex model into a more manageable one. This simplification aims to retain the essential characteristics and predictive power of the original model while reducing its size, computational cost, or complexity. The primary goals of model reduction are to improve efficiency, prevent overfitting, and enhance interpretability.

There are various techniques used for model reduction. One common approach is feature selection, where irrelevant or redundant features are identified and removed from the dataset. Another method is dimensionality reduction, which transforms the original high-dimensional data into a lower-dimensional representation while preserving as much of the important information as possible. Techniques like Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) fall under this category.

Model reduction can also involve simplifying the model's structure, such as pruning decision trees or reducing the number of parameters in a neural network. In the context of complex systems, model reduction might involve approximating differential equations with simpler forms or discretizing continuous processes. The choice of reduction technique depends on the specific model, the data, and the desired outcome. A successfully reduced model is one that is simpler and more efficient without a significant loss in accuracy or performance on unseen data.