Maswali ya Matrices ya Kuchanganyikiwa

Majibu na maelezo huonekana unapojibu maswali.

1. Katika matrix ya kuchanganyikiwa kwa binary, Hasi ya Uongo ni nini?
   • Kesi chanya ambayo mtindo ulitabiri kimakosa kuwa hasi
   • Hasi iliyotabiriwa kwa usahihi kama hasi
   • A hasi iliyotabiriwa kimakosa kama chanya
   • Chanya iliyotabiriwa kwa usahihi kama chanya
2. Usahihi huhesabiwa kama ifuatavyo:
   • TP / (TP + FN)
   • TP / (TP + FP)
   • TN / (TN + FP)
   • (TP + TN) / jumla
3. Je, ni kipimo gani kinachopima uwiano wa mambo chanya ambayo mtindo unabainisha kwa usahihi?
   • Usahihi
   • Umaalumu
   • Kumbuka
   • Usahihi
4. Katika matrix ya machafuko kwa shida na madarasa 5, seli za diagonal zinawakilisha nini?
   • Utabiri usio sahihi
   • Chanya za uwongo pekee
   • Jumla ya idadi ya sampuli
   • Utabiri sahihi kwa kila darasa
5. Kwa nini usahihi pekee unaweza kupotosha bila matrix ya mkanganyiko?
   • Huficha usawa kati ya aina tofauti za makosa kama vile chanya za uwongo na hasi za uwongo
   • Usahihi daima ni makosa
   • Haiwezi kuhesabiwa kwa matatizo ya binary
   • Inahitaji uwezekano badala ya lebo
6. Alama ya F1 ndio maana ya sauti ambayo metriki mbili?
   • Usahihi na maalum
   • Usahihi na kukumbuka
   • Sensitivity na maalum
   • Chanya za kweli na hasi za kweli
7. In a binary confusion matrix, which count is a False Positive?
   • An actual positive that the model predicts as positive
   • An actual positive that the model predicts as negative
   • An actual negative that the model predicts as positive
   • An actual negative that the model predicts as negative
8. Why should you check a library's confusion-matrix axis labels before calculating precision or recall?
   • The diagonal always contains false positives
   • Axis order changes the number of observations in the dataset
   • Precision and recall use no confusion-matrix cells
   • Libraries may place true classes in rows and predicted classes in columns, or use a different orientation