What are the techniques of feature selection?

a$.$

Supervised

b$.$

Unsupervised

c$.$

Both of these

d$.$

No one

What is Feature Selection?

a$.$

method of reducing the input variable to your model by using only relevant data

b$.$

getting rid of noise in data

c$.$

using extra features in data

d$.$

finding best features in data

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Why are the benefits of Feature Selection?

a$.$

to understand the subject better

b$.$

to find the better solution

c$.$

to judge the solution

d$.$

to find relevant and useful information from the data.

What are different types of Feature Selection Methods?

a$.$

$5$ types

b$.$

$4$ types

c$.$

$2$ types

d$.$

$3$ types

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Why are the differences between different methods?

a$.$

Filter is having high overfitting

b$.$

Wrapper have high computation time

c$.$

ANNOVA is Embedded method

d$.$

All are using the whole time of resources

What are Filter Methods?

a$.$

a type of supervised learning

b$.$

a type of unsupervised learning

c$.$

a feature selection method

d$.$

a dimensionality type

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Why are the benefits of Feature Selection?

a$.$

to understand the subject better

b$.$

to find the better solution

c$.$

to judge the solution

d$.$

to find relevant and useful information from the data.

What are Wrapper Methods?

a$.$

a type of Java method

b$.$

a type of class

c$.$

a feature selection method

d$.$

a machine learning approach

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How many types of Wrapper methods are there?

a$.$

$2$

b$.$

$3$

c$.$

$4$

d$.$

$5$

Companies use predictive analytics models to forecast inventory, manage resources, and operate more efficiently.?

a$.$

True

b$.$

False

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Which of the following is a predictive model?

a$.$

Clustering

b$.$

Regression

c$.$

Summarization

d$.$

Association rules

Identify the one in which dimensionality reduction reduces $?$

a$.$

Performance

b$.$

Entropy

c$.$

Stochastic

d$.$

Collinearity

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Which of the following techniques can be used to reduce the dimensions of the population?

a$.$

Exploratory Data Analysis

b$.$

Principal Component Analysis

c$.$

Exploratory Factor Analysis

d$.$

Cluster Analysis

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

a$.$

True

b$.$

False

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Dimensionality reduction algorithms are one of the possible ways to reduce the computation time required to build a mode $?$

a$.$

True

b$.$

False

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

a$.$

Removing columns that have too many missing values

b$.$

Removing columns that have high variance in data

c$.$

Removing columns with dissimilar data trends

d$.$

None of these

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

a$.$

True

b$.$

False

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

a$.$

PCA

b$.$

FCA

c$.$

Stochastic

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High$-$dimensional data can suffer from the curse of dimensionality $?$

a$.$

True

b$.$

False

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?

a$.$

Yes

b$.$

True

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

a$.$

True

b$.$

false