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- 1. Multiple Choice
In K-NN, is the query time longer than the training time?
Yes
No
- 2. Multiple Choice
Which of the following options is true about the k-NN algorithm?
It can only be used for classification
It can only be used for regression
It can be used for both classification and regression
- 3. Multiple Choice
Which of the following is true about Manhattan distances?
It can be used for continuous variables
It can be used for categorical variables
It can be used for categorical as well as continuous variables
- 4. Multiple Choice
What are the appropriate things to do with k-NN when you have a noisy dataset?
Increase the value of K
Decrease the value of K
K does not depend on the noise
None of these
- 5. Multiple Choice
In K-NN, what is the effect of increasing/decreasing the value of K?
The boundary becomes smoother with increasing values of K
Smoothness of the boundary does not depend on the value of K
The boundary becomes smoother with decreasing values of K
None of these
- 6. Multiple Choice
For embedded applications (i.e. running on a smartphone), what is the most appropriate family of algorithm?
Eager learners
Lazy learners
- 7. Multiple Choice
Given d the distance between a point of the dataset and the query point, which of the following weight functions is appropriate for Distance-Weighted k-NN?
w = exp( -d )
w = log ( min ( 0.25 * d, 1 ) )
w = -d
- 8. Multiple Choice
Which of the following statements is true for k-NN classifiers?
The classification accuracy is better with larger values of k
The decision boundary is linear
The decision boundary is smoother with smaller values of k
k-NN does not require an explicit training step
- 9. Multiple Choice
The curse of dimensionality only affects k-NN?
True
False
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