Guide-yakabatana mibvunzo · Pakati Nhanho

Anomaly Detection Quiz

Ongorora kuti nei kusarudzika kwakaoma kuisa mazita, kuti Isolation Sango uye nzira dzenzvimbo-dzenzvimbo dzakasiyana, uye nei mabhesi ekutanga achigona kudonha.

Nzira dzekutungamira dzinoenderanaKuonekwa kweZvinhu Zvisina Kururama
Mubvunzo 1 ye 8

Sei mamodhi ekuona anomaly achiwanzo dzidziswa pane 'zvakajairika' data pane kunzi anomalies?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Sei mamodhi ekuona anomaly achiwanzo dzidziswa pane 'zvakajairika' data pane kunzi anomalies?
    • Zvakajairwa data hazvibvumidzwe kuisa zita
    • Models haigone kugadzirisa anomalies zvachose
    • Normal data haidi computation
    • Anomalies haiwanzo uye yakanyorwa mienzaniso ishoma
  2. Sango reIsolation rinoratidza sei kusanzwisisika?
    • Anomalies akapatsanurwa mune mashoma mashoma akatsemuka, achipa mapfupi nzira kureba
    • Anomalies inoda kupatsanurwa kwakawanda kuti uzviparadzanise
    • Inoyera ruvara rweimwe data point
    • Inounganidza mapoinzi nemavara avo kutanga
  3. Kana autoencoder ikashandiswa kuona kusanzwisisika, chii chinoratidza kusanzwisisika?
    • Kukanganisa kwekuvaka patsva
    • Kukanganisa kwekuvaka patsva pane zvausina kujaira
    • Nguva yekudzidzira inokurumidza
    • Mamwe ma layer mune network
  4. Nzira yeLocal Outlier Factor inonyanya kutsamira pai?
    • Kurongeka kwearufabheti yezvimiro
    • Saizi yese yefaira re dataset
    • Kuwanda kwemapoinzi kwakatenderedza nzvimbo yakapihwa
    • Rondedzero yakagadziriswa yemhosva dzinozivikanwa dzekubiridzira
  5. Nderipi dambudziko rechokwadi kune akaiswa anomaly yekuona masisitimu?
    • Anomalies haimbochinji nekufamba kwenguva
    • Normal data inogara yakanyorwa zvakakwana
    • Outliers anogara ari mazhinji e data
    • Tsanangudzo ye'zvakajairika' inogona kukukurwa, ichida kuchinjika
  6. Ndechipi chishandiso chiri chekare chekushandisa chekuona anomaly?
    • Kutsvaga zvehutsotsi zvekadhi rechikwereti
    • Kuronga mapikicha nemaarufabheti
    • Kushandura mavara pakati pemitauro
    • Kudzvanya mavhidhiyo mafaera
  7. What does the contamination setting commonly represent in an Isolation Forest workflow?
    • The number of training trees that contain audio
    • An assumed or expected proportion of outliers used for thresholding
    • The probability a record was encrypted
    • The average depth of a normal point
  8. Why should anomaly scores be reviewed with domain context?
    • An anomaly score always proves fraud
    • All normal data has an identical distribution
    • Rare but valid events can look unusual, and errors have different costs
    • A model score replaces an investigation