Was ist Loss Function (Verlustfunktion)

Definition

A Loss Function is a mathematical method in Machine Learning that measures the discrepancy between a model's predictions and the actual target values. It quantifies a model's error and serves as the foundation for optimizing model parameters during training.

Explanation

In the B2B and SaaS landscape, loss functions play a pivotal role in developing precise predictive models for calculating lead probabilities or revenue forecasts. During model training, an optimization algorithm systematically aims to minimize the value of this function. A lower loss value signifies that the model's predictions align more accurately with actual historical sales data. Depending on the specific use case, various mathematical approaches are employed, such as Mean Squared Error (MSE) for revenue forecasting or Cross-Entropy for lead classification. The judicious selection of the appropriate loss function significantly impacts the reliability of AI-driven predictions in day-to-day sales operations.