Maswali yanayounganishwa na mwongozo · Rahisi Kiwango

Maswali ya Misingi ya Kujifunza kwa Mashine

Kuainisha kazi za kimatendo za kujifunza kwa mashine, linganisha misingi, linda data ya jaribio na ubainishe jumla au mapungufu ya utumiaji ambayo wastani yanaweza kuficha.

Njia za mwongozo zinazohusianaMisingi ya Kujifunza kwa Mashine
Swali 1 ya 8

A store wants to estimate next week’s sales as a dollar amount from past sales and calendar features. Which task is this?

Hakiki kila swali8 maswali

Majibu na maelezo huonekana unapojibu maswali.

  1. A store wants to estimate next week’s sales as a dollar amount from past sales and calendar features. Which task is this?
    • Regression
    • Uainishaji
    • Kuunganisha
    • Text generation
  2. A support team assigns each incoming message to one of five known issue labels. Which task matches that goal?
    • Kuunganisha
    • Uainishaji
    • Regression
    • Dimensionality reduction
  3. An analyst groups unlabeled news articles by similarity without predefined topics. Which task is being used?
    • Uainishaji
    • Regression
    • Kuunganisha
    • Forecasting
  4. A model scores 98% on training records but performs poorly on new records. What is the main concern?
    • It has too few input columns by definition
    • It has proved that the labels are correct
    • It is necessarily a clustering model
    • It may fit the training examples well without generalizing to new inputs
  5. Why should a team keep a final test set out of repeated model tuning?
    • Repeated tuning can adapt decisions to the test set, weakening it as an independent check
    • The test set is only for fitting model parameters
    • A test set prevents selecting a metric
    • The training set must always be smaller
  6. A forecasting team compares a complex model with “use yesterday’s value” before adopting it. What purpose does that simple rule serve?
    • A substitute for monitoring after deployment
    • A baseline showing whether added complexity provides useful information
    • A way to relabel regression as classification
    • A guarantee that the complex model is fair
  7. A deployed model suddenly receives temperatures in Fahrenheit after training on Celsius. Which workflow lesson applies?
    • Strong test accuracy makes input units irrelevant
    • Only the model architecture can cause failures
    • Errors can arise from production preparation or changed input conventions
    • Deployment removes the need to check inputs
  8. A classifier has strong average accuracy but misses nearly every rare emergency case. What should the team do?
    • Accept it because average accuracy is high
    • Tune on the final test set until the score improves
    • Remove the rare cases from evaluation
    • Inspect failure cases and choose metrics that reflect the consequences of mistakes