A data scientist wants to predict a person's travel destination. The options are: - Branson, Missouri, United States - Mount Kilimanjaro, Tanzania - Disneyland Paris, Paris, France - Sydney Opera…

The correct answer is A. Linear discriminant analysis. You need a supervised multiclass classification model to predict one of the four labeled destinations. Linear Discriminant Analysis is designed for such tasks, finding the linear boundaries that best separate the known destination classes.

Machine Learning

Question

A data scientist wants to predict a person's travel destination. The options are:

Branson, Missouri, United States
Mount Kilimanjaro, Tanzania
Disneyland Paris, Paris, France
Sydney Opera House, Sydney, Australia

Which of the following models would best fit this use case?

Options

ALinear discriminant analysis
Bk-means modeling
CLatent semantic analysis
DPrincipal component analysis

How the community answered

(34 responses)

A
82% (28)
B
9% (3)
C
3% (1)
D
6% (2)

Explanation

You need a supervised multiclass classification model to predict one of the four labeled destinations. Linear Discriminant Analysis is designed for such tasks, finding the linear boundaries that best separate the known destination classes.

Topics

#linear discriminant analysis#multi-class classification#supervised learning#algorithm selection

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