Week 2 - ICT202 Machine Learning

Question 1

The categories in which Machine learning approaches can be traditionally categorized are ______

Accepted Answer

All of Above

Question 2

Which statement is true about Neural network and linear regression models?

Accepted Answer

Both models require input attributes to be numeric

Question 3

Which of the following statements is not true about optimal separating hyperplane?

Accepted Answer

The optimal hyperplane can't perfectly classify all data and stay distant.

Question 4

K-Nearest Neighbors (KNN) is classified as what type of machine learning algorithm?

Accepted Answer

Instance-based learning

Question 5

Instances are represented by this pair

Accepted Answer

Attributes and values

Question 6

Which of the following statements is not true about SVM?

Accepted Answer

Choosing an appropriate kernel function is easy

Question 7

Which of these is a quality of linear regression?

Accepted Answer

Models are applied globally

Question 8

When performing regression or classification, which of the following is the correct way to preprocess the data?

Accepted Answer

Normalize the data --> PCA --> Training

Question 9

Another name for an output attribute.

Accepted Answer

Independent variable

Question 10

Which of the following is an example of feature extraction?

Accepted Answer

All of the above

Question 11

The supervised learning problems can be grouped as_______.

Accepted Answer

both A and B

Question 12

A common type of distance measure used in KNN is the _________ distance.

Accepted Answer

Euclidean

Question 13

The effectiveness of an SVM depends upon:

Accepted Answer

All of these

Question 14

Why are decision trees commonly used for?

Accepted Answer

Classification

Question 15

The average squared difference between classifier predicted output and actual output.

Accepted Answer

Mean squared error

Question 16

Which of the following is a disadvantage of decision trees?

Accepted Answer

Decision trees are prone to be overfit

Question 17

Support Vector Machine (SVM) is a discriminative classifier defined by a separating hyperplane

Accepted Answer

Yes

Question 18

Support vector machines cannot be used for regression.

Accepted Answer

False

Question 19

The __________ of a dataset represents the number of features in the dataset.

Accepted Answer

Dimensionality

Question 20

_________ is the machine learning algorithms that can be used with labeled data

Accepted Answer

Clustering

Questions 20