Was ist Overfitting

Definition

Overfitting describes a phenomenon in machine learning where a model becomes overly tailored to the specific details and noise within its training data. While this allows the model to achieve excellent results on the familiar training data, it loses its generalization capability and performs poorly when encountering new, unseen data.

Explanation

In the B2B and SaaS context, overfitting poses a significant risk, as historical data is often limited or heavily influenced by seasonal effects. For instance, if a predictive model used for deal scoring memorizes irrelevant details from the training data, it will deliver inaccurate forecasts for new potential customers. In such cases, the model hasn't developed a deeper understanding of the true success factors; it has merely learned the noise. To detect overfitting early, datasets are split into separate training and test sets. Effective countermeasures in the B2B software development process include techniques such as regularization, reducing model complexity, data augmentation, and early stopping of the training process.