Guide-yakabatana mibvunzo · Pakati Nhanho

Kirasi Kusaenzana uye Resampling Quiz

Miedzo nguva yekuenzanisazve kana kurema makirasi asingawanzo, kuti kuburitswa kwedata kunomuka sei, uye ndeapi metrics anoramba achibatsira pasi pekusaenzana kwekirasi.

Nzira dzekutungamira dzinoenderanaKirasi Imbalance uye Resampling
Mubvunzo 1 ye 8

Nei kunyatsojeka kuri metric yakashata kune yakanyanya kusaenzana kurongedza dambudziko?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Nei kunyatsojeka kuri metric yakashata kune yakanyanya kusaenzana kurongedza dambudziko?
    • Muenzaniso unogona kukwira zvakanyanya nekungogara uchifanotaura kirasi yeruzhinji
    • Kururama hakugone kuverengerwa kana makirasi asina kuenzana
    • Kururama kunoshanda chete pakudzoreredza
    • Inoda data yakanyatsorongeka kuti itsanangurwe
  2. Chii chinoita SMOTE?
    • Inodzima masamples emhando zhinji chero nguva
    • Inogadzira masampuli mashoma ekugadzira nekubatanidza pakati pevavakidzani
    • Inowedzera mwero wekudzidza kumakirasi asingawanzo
    • Inoshandura dambudziko rezvirongwa kuita regression
  3. Ndeipi nzira inobata kusaenzana PASI kushandura huwandu hwemienzaniso yekudzidziswa?
    • Random oversampling
    • Kutevedzera ruzhinji
    • Kirasi uremu mubasa rekurasikirwa
    • SMOTE
  4. Ndeipi njodzi yakakosha yekushandisa oversampling usati wapatsanura data rako muchitima uye bvunzo seti?
    • Zvinoita kuti kudzidzisa kunonoke
    • Inobvisa kirasi yeruzhinji
    • Zvinogara zvichikonzera underfit
    • Akadhindwa kana ekugadzira zvidiki mapoinzi anogona kudonhedza muyedzo seti, ichiwedzera zvibodzwa
  5. Chii chakanyanya kuderera chezvisina kurongwa undersampling?
    • Inorasa data inogona kubatsira yakawanda-kirasi
    • Inogadzira data remanyepo ringave risingaite
    • Inoshanda chete kune data data
    • Nguva dzose inowedzera nguva yekudzidzira
  6. Ndeipi metric inonyanya kukodzera pakuongorora modhi pane isingawanzo-positive data?
    • Kururama kwese
    • Precision-Yeuka AUC kana F1 mamaki
    • Kureva kukanganisa kwakapetwa kaviri
    • Nhamba yenguva yekudzidziswa
  7. What does adjusting the decision threshold after training change?
    • The number of features in the model
    • The class proportions in the training data
    • The trade-off between which examples are labeled positive and negative without changing the training examples
    • The model’s learned weights automatically
  8. Which metric does the guide name as an additional option for rare-positive classification?
    • Mean squared error only
    • BLEU score
    • Silhouette coefficient
    • Matthews Correlation Coefficient (MCC)