F1 Score vs ROC-AUC vs Accuracy
Besides the F1 score, there are other metrics like accuracy, AUC-ROC, etc which can be used to evaluate model performance. The choice of metric depends on the problem at hand. There is no one-size-fits-all all. More than often a combination of metrics are looked at to gauge the overall performance of the model. Below are general rules that are followed :
F1 vs Accuracy
If the problem is balanced and you care about both positive and negative predictions, accuracy is a good choice. If the problem is imbalanced(a lot of negative cases compared to positive) and we need to focus on positive cases the F1 score is a good choice.
F1 vs AUC-ROC
AUC-ROC helps us to understand the ability of the model to discriminate between positive and negative instances overall, regardless of class imbalance at different thresholds while the F1 score evaluates the performance of the model at a particular threshold. Hence one might use F1 for class-specific evaluation while AUC-ROC for overall assessment of model.
F1 Score in Machine Learning
The F1 score is an important evaluation metric that is commonly used in classification tasks to evaluate the performance of a model. It combines precision and recall into a single value. In this article, we will understand in detail how the F1 score is calculated and compare it with other metrics.