quiz bu lëkkale ak tegtal · Diggante Niveau

Quiz bandit yu am ay gànnaay yu bari

Saytu seet, jaay doole, réccu, epsilon-bërëb, UCB wóolu bonus, Thompson sampling ak màndarga yi ci nekk.

Yooni njiit yu jëm ciBandits yu am ay weñ yu bari
Laaj 1 de 8

Li gëna am solo ci jafe-jafe bandit yu bari yi mooy:

Xoolaatal laaj bu nekk8 laaj

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

  1. Li gëna am solo ci jafe-jafe bandit yu bari yi mooy:
    • Exploration ak jëfandikoo
    • Bias ak faraas
    • Precision ak fàttaliku
    • Taggat ak jël doggal
  2. Ci jafe-jafey bandit, 'réccu' dafay tekki:
    • Memoire bi algorithm bi di jëfandikoo
    • Diggante neexal biñ dajale ak jouer loxo bi gëna baax saa yu nekk
    • Limu gànnaay yi jàppandi
    • Waxtu tàggat model
  3. Lan la pexem epsilon bi di def?
    • Deel tann loxo bi am moyenne bi gëna kawe
    • musul xool benn loxo
    • Dafay gëna jëfandikoo loxo bi gëna baax waaye dafay banneexu ci anam wu mujjee ak probabilite epsilon
    • Dafay xëcc loxo bu nekk limu yoon yu tolloo ba fàww
  4. Xeetu Limite de Confiance bu Kawe (UCB) dafa topp njàngalem:
    • Pessimism ci kanamu ñàkka wóor
    • Baña bàyyi xel ci ñàkka wóor
    • Tanneef yu bari rek
    • Optimism ci jano ak ñàkka xam
  5. Thompson dafay tànn loxo ci:
    • jël misaal ci gëm-gëmu ginaaw bu loxo bu nekk ak jouer misaal bi gëna baax
    • Deel tann loxo bi gëna néew
    • Tann ay loxo ci xeetu rond bu takku
    • Wàññi li ñuy ñàkk ci tàggat yaram
  6. Lan moo wuutale bandit contextuel ak bandit standard?
    • Dafay baña bàyyi xel ci neexal yi
    • Dafay jëfandikoo màndarga yi am ci jamono jii ngir leeral tànneef bi
    • Benn loxo kese la am
    • Mënul jàng lu yàgg
  7. In UCB, which arm usually receives a larger exploration bonus?
    • An arm whose sample count is largest
    • An arm with no uncertainty by definition
    • An arm that has been sampled relatively few times
    • An arm with a negative reward only
  8. What does logarithmic regret growth indicate over a long run?
    • The algorithm receives zero reward after each round
    • The best arm changes at every step
    • Exploration stops before the first observation
    • The cumulative gap from always choosing the best arm grows much slower than the number of rounds