F score, also called F-measure or F-1 score, is a measurable parameter used in data science to measure a binary classification model’s accuracy. The F1 score combines precision and recall, two common evaluation measures in binary classification, into a single metric using a weighted harmonic mean.
Precision is the number of true positives out of all positive predictions made, while recall is the number of true positive predictions out of all actual positive cases in the dataset. The F score uses both these metrics to measure the accuracy of the model.
Formula for F score: F = 2 * (precision * recall) / (precision + recall)
Applications of F Score in AI
F score is used in the AI field in various ways.
1. Fraud Detection
The F score is often used to measure fraud detection models’ accuracy. Since fraud is relatively uncommon in financial datasets, the F score is an excellent measure of the effectiveness of models that try to find it.
2. Medical Diagnosis
The F score can be utilized to assess how well models that diagnose diseases like cancer or heart disease work. These models require high precision and accuracy and the F score is a useful measure to assess the model effectiveness.
3. Natural Language Processing
The F-score is a widely used performance metric in natural language processing tasks such as sentiment analysis and text classification. In these situations, the F score can be used to evaluate how well models classify text into different groups.
4. Image Classification
The F score is another way to measure accuracy of image classification models. These models put images into different groups, like scenes or objects, and they must be very accurate and precise to work well.
