Maswali yanayounganishwa na mwongozo · Kati Kiwango

Maswali ya Majirani wa K-Karibu Zaidi

Inashughulikia upigaji kura wa majirani, kuongeza vipengele, gharama ya utabiri, uchaguzi wa kipimo na mabadiliko ya vitendo wakati wa kuhifadhi mifano ya mafunzo.

Njia za mwongozo zinazohusianaK Majirani wa Karibu
Swali 1 ya 8

KNN inaainisha vipi nukta mpya ya data?

Hakiki kila swali8 maswali

Majibu na maelezo huonekana unapojibu maswali.

  1. KNN inaainisha vipi nukta mpya ya data?
    • Kwa kupiga kura nyingi za majirani zake wa karibu wa K
    • Kwa kufundisha mtandao wa neva juu yake
    • Kwa kukokotoa mstari wa rejista ulio na uzani
    • Kwa kugawa darasa la nasibu
  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. Kwa nini kuongeza kipengele ni muhimu kwa KNN?
    • Inafanya mafunzo haraka
    • Inapunguza idadi ya majirani zinazohitajika
    • Kwa hivyo vipengele vilivyo na safu kubwa hazitawali umbali
    • Inabadilisha K moja kwa moja
  4. Nini kitatokea ukichagua K ndogo sana, kama K=1?
    • Mfano huo unakuwa laini sana
    • Mfano hupuuza data
    • Utabiri huwa hauwezekani
    • Mfano huo unakuwa nyeti kwa kelele na unaweza kuzidi
  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. Ni teknolojia gani ya kisasa inategemea wazo la jirani-jirani?
    • Mfinyazo wa picha na JPEG
    • Utafutaji wa Vekta na kizazi kilichoboreshwa cha urejeshaji
    • Uzalishaji wa nambari bila mpangilio
    • Miundo ya lahajedwali
  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