Guide-yakabatana mibvunzo · Pakati Nhanho

K-Nearest Neighbours Quiz

Inovhara kuvhota kwevavakidzani, kuwedzera kwechimiro, mutengo wekufembera, metric sarudzo, uye inoshanda tradeoffs paunenge uchichengeta mienzaniso yekudzidziswa.

Nzira dzekutungamira dzinoenderanaK Vavakidzani Vepedyo
Mubvunzo 1 ye 8

KNN inoronga sei poindi yedata nyowani?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. KNN inoronga sei poindi yedata nyowani?
    • Nekutora vhoti yakawanda yevavakidzani vayo vepedyo veK
    • Nokudzidzisa neural network pairi
    • Nekukombuta mutsara wakaremerwa wekudzoka
    • Nekupa kirasi isina kurongeka
  2. Why is KNN often called a “lazy learner”?
    • It never stores training data
    • It stores training examples and can defer much model-specific work until prediction, though indexes may be built during fitting
    • It always trains a deep neural network first
    • It does no work during either fitting or prediction
  3. Nei kuyera kwechimiro kwakakosha kuKNN?
    • Zvinoita kuti kudzidzira kukurumidze
    • Inoderedza nhamba yevavakidzani inodiwa
    • Saka maficha ane mahombe siyana haatongi chinhambwe
    • Inochinja K otomatiki
  4. Chii chinoitika kana ukasarudza K=1 mudiki-diki?
    • Iyo modhi inova yakanyanya kutsetseka
    • Iyo modhi inofuratira iyo data
    • Kufanotaura kunova kusingabviri
    • Iyo modhi inova nehanya kune ruzha uye inogona kuwandisa
  5. In a brute-force KNN implementation, why can prediction be slow on a large dataset?
    • It may compute distances from a query to many or all stored examples
    • It must update all model weights for every query
    • It reruns the entire training dataset through a neural network
    • It cannot use an index or approximate search
  6. Ndeipi tekinoroji yemazuva ano inovimba nepfungwa yepedyo-yemuvakidzani?
    • Image compression neJPEG
    • Vector kutsvaga uye kudzoreredza-yakawedzera chizvarwa
    • Random nhamba chizvarwa
    • Spreadsheet formulas
  7. Why should a feature scaler be fit only on the training split before KNN evaluation?
    • To make the distance metric ignore every feature
    • To guarantee all classes have equal counts
    • To avoid using information from validation or test examples when setting the scale
    • To remove the need to choose K
  8. How can KNN make a prediction for a regression task?
    • Choose the most common class label only
    • Fit a new deep network for each query
    • Ignore the stored examples and use a fixed intercept
    • Aggregate the target values of the K nearest training examples, often by averaging