Consider a dataset of 10000 medical images, consisting of 80 cancer images and 9920 normal (i.e. no cancer) images. A model correctly identifies 60 of the 80 cancer images (and incorrectly identifies the remaining 20), and correctly identifies 9880 of the 9920 normal images (and incorrectly identifies the remaining 40). Calculate the values of the true positive (TP), false negative (FN), false positive (FP), true negative (TN), precision, recall, and F1 score.
Consider a dataset of 10000 medical images, consisting of 80 cancer images and 9920 normal (i.e. no cancer) images. A model correctly identifies 60 of the 80 cancer images (and incorrectly identifies the remaining 20), and correctly identifies 9880 of the 9920 normal images (and incorrectly identifies the remaining 40). Calculate the values of the true positive (TP), false negative (FN), false positive (FP), true negative (TN), precision, recall, and F1 score.
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Consider a dataset of 10000 medical images, consisting of 80 cancer images and 9920 normal (i.e. no cancer) images. A model correctly identifies 60 of the 80 cancer images (and incorrectly identifies the remaining 20), and correctly identifies 9880 of the 9920 normal images (and incorrectly identifies the remaining 40). Calculate the values of the true positive (TP), false negative (FN), false positive (FP), true negative (TN), precision, recall, and F1 score.
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