Accuracy is the difference between the output or prediction of an algorithm and the true value. For numerical results it is often measured using the mean square error (the average of the square of the difference) or absolute difference. For classifications there are several different kinds of accuracy measures that are important, including precision and recall for binary decisions, so the word 'accuracy can be ambiguous.
Used in Chap. 8: page 115; Chap. 9: pages 130, 134; Chap. 12: page 192; Chap. 14: page 228; Chap. 15: pages 246, 252; Chap. 18: pages 308, 309; Chap. 19: pages 320, 322, 324, 326; Chap. 20: pages 337, 342; Chap. 21: page 353; Chap. 23: pages 387, 391