机读格式显示(MARC)

• 000 01608cam a2200265 a 4500

• 001 0000926847

• 003 CAL

• 005 20211015113100.0

• 008 210616s2020 nyua b 001 0 eng d

• 020 __ |a 9780198864165 (hbk.)

• 040 __ |a CEPC1 |c CEPC1

• 090 __ |a TP311.13/S648

• 093 __ |a TP311.13 |2 5

• 099 __ |a CAL 022021079340

• 100 1_ |a Smith, Gary.

• 245 14 |a The phantom pattern problem : |b the mirage of big data / |c Gary Smith, Jay Cordes.

• 264 _1 |a Oxford ; |a New York, NY : |b Oxford University Press, |c 2020.

• 300 __ |a 227 p. : |b ill. ; |c 24 cm.

• 504 __ |a Includes bibliographical references (pages 217-224) and index.

• 520 __ |a Pattern-recognition prowess served our ancestors well, but today we are confronted by a deluge of data that is far more abstract, complicated, and difficult to interpret. The number of possible patterns that can be identified relative to the number that are genuinely useful has grown exponentially - which means that the chances that a discovered pattern is useful is rapidly approaching zero.Patterns in data are often used as evidence, but how can you tell if that evidence is worth believing? We are hard-wired to notice patterns and to think that the patterns we notice are meaningful. Streaks, clusters, and correlations are the norm, not the exception. Our challenge is to overcome our inherited inclination to think that all patterns are significant, as in this age of Big Data patterns are inevitable and usually coincidental.

• 650 _0 |a Big data.

• 650 _0 |a Data mining.

• 700 1_ |a Cordes, Jay.

• 920 __ |a 242450 |z 1

• 998 __ |a CALIS