Guide-yakabatana mibvunzo · Pakati Nhanho

AI muMutengi Churn Prediction Quiz

Wongorora kuti machurn modhi anoshandisa sei nhoroondo dzekuzvibata, tsanangura mafambiro ekufanotaura, kubata mhedzisiro isina kuenzana, uye kutungamira kuedza kuchengetedza.

Nzira dzekutungamira dzinoenderanaAI Mune Mutengi Churn Prediction
Mubvunzo 1 ye 8

Chii chinonzi 'customer churn' chinorevei?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Chii chinonzi 'customer churn' chinorevei?
    • Vatengi vari kukanzura kana kurega kushandisa chigadzirwa kana sevhisi
    • Vatengi vanowedzera mari yavo
    • Maitiro ekukwira vatengi vatsva
    • Mhando ye email yekutengesa
  2. Imhando ipi yedambudziko rekudzidza muchina iro churn kufanotaura kunowanzo kurongwa se?
    • Kuungana kusingatarisirwi
    • Kurongeka kwakatariswa uchishandisa data yakanyorwa kare
    • Kusimbisa kudzidza kubva kune mibairo
    • Mufananidzo chizvarwa
  3. Nei kunyatsojeka kuri metric yakashata yemhando dzechurn?
    • Kururama hakubviri kuverengera
    • Kurongeka kunoshanda pamifananidzo chete
    • Churn dataset haina kuenzana, saka kufanotaura 'hapana churn' kune wese munhu anogona kutaridzika chaizvo
    • Churn haina mhedzisiro inoyerwa
  4. Chii chinonzi 'data leakage' mune ino mamiriro?
    • Vatengi vanogovana mapassword avo
    • Kurasikirwa nemarekodhi evatengi kune ma hackers
    • Slow database mibvunzo
    • Chinhu chinoisa mhedzisiro netsaona, senge zuva rekudzima, inflating performance
  5. Ndeipi algorithm mhuri inonyanya kufarirwa kune tabular churn data?
    • Gradient-yakawedzera sarudzo miti seXGBoost uye LightGBM
    • Convolutional neural network
    • Recurrent neural network yeaudio
    • K-zvinoreva kubatanidza chete
  6. Chii chinonzi 'uplift modeling' chinowedzera kupfuura fungidziro yechurn?
    • Inoita kuti modhi imhanye nekukurumidza
    • It estimates which customers may be more likely to benefit from an intervention
    • Inobvisa kudiwa kwe data chero ipi zvayo
    • Inoshanda chete kune vatengi vatsva
  7. Which feature group can help a churn model detect weakening customer engagement?
    • The color of the customer’s profile image
    • A feature recorded only after cancellation has occurred
    • Recent activity and usage frequency, alongside contract and support history
    • The model’s GPU temperature during training
  8. What should a team define when labeling a churn model’s prediction target?
    • The color scheme of the account dashboard
    • The number of features before collecting customer history
    • A fixed decision threshold before model training begins
    • A clear prediction horizon, such as whether a customer will leave within the next 30 or 90 days