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---
title: "Regularization"
slug: "regularisierung-regularization"
type: "glossary"
---
Definition
Regularization refers to a suite of techniques in Machine Learning designed to prevent overfitting by strategically constraining a model's complexity during its training phase. This is typically achieved by adding a penalty term to the objective function, which penalizes overly large model parameter weights.
Explanation
In B2B applications, developers frequently face the challenge of training models on a high volume of features with relatively sparse datasets, a scenario that significantly elevates the risk of overfitting. Regularization techniques such as L1 (Lasso) and L2 (Ridge) counteract this by either dampening the model's weight values or entirely eliminating irrelevant features. Furthermore, in deep neural networks, Dropout is commonly applied to reduce the co-dependency among neurons. This deliberate constraint forces the model to identify more robust and generalized patterns within the data. For SaaS platforms leveraging predictive systems, effective regularization translates into more reliable predictions and stable performance in real-world production environments.