Dimensionality Reduction Techniques

Dimensionality Reduction Techniques

University

10 Qs

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Dimensionality Reduction Techniques

Dimensionality Reduction Techniques

Assessment

Quiz

Computers

University

Practice Problem

Medium

Created by

Assoc.Prof, Chennai

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10 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Imagine, you have 1000 input features and 1 target feature in a machine learning problem. You have to select 100 most important features based on the relationship between input features and the target features.


Do you think, this is an example of dimensionality reduction?

Yes

No

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

It is not necessary to have a target variable for applying dimensionality reduction algorithms.

True

False

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

I have 4 variables in the dataset such as - A, B, C & D. I have performed the following actions:


Step 1: Using the above variables, I have created two more variables, namely E = A + 3 B and F = B + 5 C + D.


Step 2: Then using only the variables E and F I have built a Random Forest model.


Could the steps performed above represent a dimensionality reduction method?

No

Yes

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which of the following techniques would perform better for reducing dimensions of a data set?

Removing columns which have too many missing values

Removing columns which have high variance in data

Removing columns with dissimilar data trends

None of these

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Dimensionality reduction algorithms are one of the possible ways to reduce the computation time required to build a model.

True

False

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which of the following algorithms cannot be used for reducing the dimensionality of data?

Factor Analysis

PCA

LDA

None of these

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

PCA can be used for projecting and visualizing data in lower dimensions.

True

Fasle

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