Modellkomplexitás

Modellkomplexitás refers to the degree of intricacy or sophistication within a model. In various fields, including mathematics, computer science, and scientific modeling, this concept helps assess a model's structure, the number of parameters it employs, and the interrelationships between its components. A model with low complexity might be a simple linear equation, while a high-complexity model could be a deep neural network with millions of parameters.

The level of modellkomplexitás is often considered in relation to the problem it aims to solve. An overly simple model may fail to capture essential nuances of a phenomenon, leading to underfitting. Conversely, a model with excessive complexity might overfit the data, performing well on training examples but generalizing poorly to new, unseen data. Finding an appropriate balance is crucial for building effective and reliable models.

Techniques exist to measure and manage modellkomplexitás. Information criteria like AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) penalize models for increased complexity while rewarding goodness of fit. Regularization methods in machine learning also aim to reduce complexity by constraining parameter values. The choice of complexity is a trade-off between explanatory power and the risk of overfitting.