What Are Accuracy, Precision, Recall, And F-Score

Accuracy, Precision, Recall, and F-Score are metrics used in classification tasks to evaluate the performance of a model. Accuracy measures the proportion of correct predictions, Precision measures the proportion of true positive predictions, Recall measures the sensitivity of the model, and F-Score is the harmonic mean of Precision and Recall.

F-Score: What Are Accuracy, Precision, Recall, And F1 Score

Areas of application

  • natural language processing
  • image classification
  • medical diagnosis
  • financial risk assessment
  • quality control in manufacturing

Example

For instance, if a machine learning model is used to classify images as either cats or dogs, the F-Score could be used to evaluate how well the model is performing. If the F-Score is high, it means that the model is making both accurate and precise predictions, and is able to correctly identify most of the images as either cats or dogs.

Resources