Was ist Accuracy & F-Score

Definition

Accuracy and F-Score (often referred to as F1-Score) are key statistical metrics used to evaluate the performance and predictive power of machine learning models in classification tasks.

Explanation

Accuracy measures the proportion of correctly predicted cases relative to the total number of cases analyzed. However, this metric can be misleading when dealing with imbalanced datasets (class imbalance); for instance, if 99% of leads do not convert, a naive model that always predicts 'no conversion' would achieve 99% accuracy, yet be utterly useless. The F-Score addresses this problem by calculating the harmonic mean of Precision and Recall, thereby weighting both false positive and false negative predictions. In B2B and SaaS contexts, such as automated lead qualification, churn prediction, or spam detection, the F-Score is typically the more reliable metric for model optimization. A balanced F-Score ensures that the system operates reliably in real-world scenarios and that business-critical processes can be automated flawlessly.