Start learning 50% faster. Sign in now
Anomaly detection identifies data points that deviate significantly from the norm. In fraud detection, it flags unusual transactions, such as large withdrawals or geographically improbable activity, for further investigation. Techniques like isolation forests or Z-scores are commonly used. Option A : Logistic regression predicts binary outcomes but isn’t specialized for anomalies. Option C : Clustering groups similar data but doesn’t detect deviations effectively. Option D : Decision trees classify data but are not optimal for real-time anomaly detection. Option E : A/B testing is unrelated to fraud detection.
(20.23% of 780.31) + ? + (29.87% of 89.87) = 283
Find the ratio of the area of an equilateral triangle of side ‘a’ cm to the area of a square having each side equal to ‘a’ cm.
(1331)1/3 x 10.11 x 7.97 ÷ 16.32 =? + 15.022
? = 782.24 + 1243.97 – 19.992
390.11 ÷ 12.98 × 5.14 – 119.9 = √?
[(80.97) 3/2 + 124.95 of 8% - {(21.02/6.95) × 10.9 × 5.93}]/ 45.08 = ?
25.09 × (√15 + 19.83) = ? of 19.87 ÷ 4.03
15.2 x 1.5 + 258.88+ ? = 398.12 + 15.9
26.23 × 31.82 + 44.8% of 1200 + ? = 1520