Guide-yakabatana mibvunzo · Pakati Nhanho

Naive Bayes Classifiers Quiz

Tarisa kuti Naive Bayes inosanganisa sei makirasi ekutanga uye maficha angangoita, uye mabatiro ayo akasiyana anobata kuverenga, mabhinari maficha, uye inoenderera data.

Nzira dzekutungamira dzinoenderanaNaive Bayes Classifiers
Mubvunzo 1 ye 8

Chii chinonzi 'naive' fungidziro muNaive Bayes classifier?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Chii chinonzi 'naive' fungidziro muNaive Bayes classifier?
    • Zvimiro zvakazvimiririra zvakazvimiririra zvakapihwa kirasi
    • Makirasi anogona zvakaenzana
    • Zvinhu zvose zvinoenderera uye zvinowanzogoverwa
    • Iyo dataset yakanyatsoenderana
  2. Ndeipi theorem iyo Naive Bayes yakavakirwa pairi?
    • Iyo yepakati muganhu theorem
    • Bayes’ theorem
    • Mutemo wenhamba huru
    • The Cauchy-Schwarz inequality
  3. Nei mashandisirwo achiwanzoita sum-mukana panzvimbo pekuwanza zvingangoitika?
    • Kuita kuti modhi iite isina mutsara
    • Kuti akurumidze kudzidzira kusvika pakupasa kamwe chete
    • Kudzivirira kuderera kwenhamba kubva pakuwanza nhamba diki dzakawanda
    • Kumanikidza kuzvimiririra
  4. Nderipi dambudziko rinogadziriswa naLaplace (add-one)?
    • Inoenzanisa saizi dzekirasi
    • It normalizes inoenderera zvinhu
    • Inobvisa zvinhu zvakabatana
    • Inodzivirira chinhu chisingaoneki kubva pakumanikidza mukana we zero
  5. Ndeupi Naive Bayes musiyano wakajairika kune mazwi-kuverenga maficha mumagwaro?
    • Multinomial Naive Bayes
    • Gaussian Naive Bayes
    • Bernoulli Naive Bayes
    • Linear Naive Bayes
  6. Why might Naive Bayes be useful as a lightweight baseline?
    • Inovimbisa huchokwadi hwepamusoro pane chero dataset
    • Inokurumidza, inodudzirwa, uye inoshanda nemazvo ine data shoma
    • Inodzidzira otomatiki kudyidzana kwemaitiro
    • Izvo zvinoda hapana data yekudzidziswa
  7. What distribution does Gaussian Naive Bayes assume for each feature within a class?
    • A uniform distribution over every possible value
    • A multinomial distribution over word counts
    • A Gaussian (normal) distribution
    • A distribution that is always exactly zero
  8. Which feature representation fits Bernoulli Naive Bayes most naturally?
    • The number of times each word occurs
    • A continuous pixel intensity modeled by a normal curve
    • A sequence of audio frames
    • Whether each word is present or absent in a document