Recall Score Method: Definition, History, and Further Reading
The recall score method is an evaluation method used to measure the accuracy of a model in retrieving relevant documents from a database. It is a commonly used metric in information retrieval systems, such as text-based search engines. The recall score is calculated by dividing the number of relevant documents retrieved by the total number of relevant documents in the database. The higher the recall score, the better the accuracy of the model.
The recall score method was first proposed by R.A. Fisher in the late 1940s and has been widely used ever since. Fisher proposed the method as a way to measure the effectiveness of documents retrieved from a database. It was later adopted by many information retrieval systems as a standard method for measuring the accuracy of a model.
In recent years, the recall score method has been applied to a variety of domains. For example, it has been used to measure the accuracy of natural language processing systems in accurately detecting and categorizing text. It has also been used to measure the accuracy of image classification models in accurately detecting and recognizing objects from a database of images.
The recall score method is an important tool for evaluating the accuracy of a model. It is widely used in a variety of domains and can be a powerful metric for evaluating the performance of a model.
Further Reading
Fisher, R. A. (1948). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 14(2), 179-188.
Ribeiro, M., Singh, S., & Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135-1144). ACM.
Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer-Verlag.
Luo, G., & Chang, E. (2007). A framework for analyzing information retrieval systems using precision and recall. Information Processing and Management, 43(1), 71-88.