F1 Score

The F1 Score calculator is a useful tool for evaluating the accuracy of classification models in machine learning. It calculates the weighted average of precision and recall, considering both the number of correct samples and false positives and negatives.

The underlying formula is simple: 2 * (precision * recall) / (precision + recall). This metric is particularly useful in classification problems with imbalanced classes, where the classification of negative samples is more critical.

Here you can calculate the F1 Score of a classification model, whether on a test set or on a production data set. Additionally, our calculator provides information on how to use the F1 Score appropriately in different contexts.

Remember that the F1 Score is not an absolute measure of performance, but rather a tool for comparing models with each other. Therefore, it's essential to consider other factors, such as the precision-recall curve and the analysis of contribution of each feature.