Data can be visualized using?

a$.$

graphs

b$.$

charts

c$.$

maps

d$.$

All of the above

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Which of the intricate techniques is not used for data visualization?

a$.$

Bullet Graphs

b$.$

Bubble Clouds

c$.$

Fever Maps

d$.$

Heat Maps

Data science is the process of diverse set of data through $?$

a$.$

organizing data

b$.$

processing data

c$.$

analyzing data

d$.$

All of the above

In which step of Knowledge Discovery, multiple data sources are combined?

a$.$

Data Cleaning

b$.$

Data Integration

c$.$

Data Selection

d$.$

Data Transformation

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What is the use of data cleaning?

a$.$

to remove the noisy data

b$.$

correct the inconsistencies in data

c$.$

transformations to correct the wrong data.

d$.$

All of the above

Point out the correct statement.

a$.$

Raw data is original source of data

b$.$

Preprocessed data is original source of data

c$.$

Raw data is the data obtained after processing steps

d$.$

None of the mentioned

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Which of the following is performed by Data Scientist?

a$.$

Define the question

b$.$

Create reproducible code

c$.$

Challenge results

d$.$

All of the mentioned

A collection of information about a related topic is referred to as a$\_\_\_\_\_\_\_\_\_\_$

a$.$

Visualization

b$.$

Analysis

c$.$

Conclusion

d$.$

Data

For unsupervised learning we have $\_\_\_\_$ model.

a$.$

interactive

b$.$

predictive

c$.$

descriptive

d$.$

Prescriptive

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What is Machine learning?

a$.$

The autonomous acquisition of knowledge through the use of computer programs

b$.$

The autonomous acquisition of knowledge through the use of manual programs

c$.$

The selective acquisition of knowledge through the use of computer programs

d$.$

The selective acquisition of knowledge through the use of manual programs

Supervised learning and unsupervised clustering both require which is correct according to the statement.

a$.$

input attribute

b$.$

hidden attribute

c$.$

output attribute

d$.$

categorical attribute

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Following are the types of supervised learning$\_\_\_\_\_\_\_\_$

a$.$

Regression

b$.$

classification

c$.$

subgroup discovery

d$.$

All of above

Which of the following is the best machine learning method?

a$.$

Accuracy

b$.$

Scalable

c$.$

Fast

d$.$

All of above

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Following are the descriptive models$\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_$

a$.$

classification

b$.$

clustering

c$.$

association rule

d$.$

Both $1$ and $2$

How many types of Machine Learning Techniques?

a$.$

$3$

b$.$

$5$

c$.$

$7$

d$.$

$9$

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Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging?

a$.$

Random Forest

b$.$

Regression

c$.$

Classification

d$.$

Decision Tree

Which of the following is not numerical functions in the various function representation of Machine Learning?

a$.$

Neural Network

b$.$

Case$-$based

c$.$

Linear Regression

d$.$

Support Vector Machines

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Which supervised learning technique can process both numeric and categorical input attributes?

a$.$

Linear regression

b$.$

Bayes classifier

c$.$

Ogistic regression

d$.$

None of the Above

What is the benefit of Feature Extraction?

a$.$

Dimensionality Reduction

b$.$

Overfitting

c$.$

Underfitting

d$.$

Efficient utilization of resources.

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Why feature is important

a$.$

To see the datasets

b$.$

To make it attractive

c$.$

To reduce the dimensionality of data

d$.$

to find good data

Feature Transformation is

a$.$

Algebraic Transformation

b$.$

Machine Learning Transformation

c$.$

Mathematical Transformation

d$.$

Statistical Transformation

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What is the need for Feature Transformation:$-$

a$.$

To improve accuracy

b$.$

To find convergence

c$.$

To increase the size of data

d$.$

To increase the performance of model.

What is the purpose of feature selection?

a$.$

to remove irrelevant or redundant features

b$.$

to make noise in data

c$.$

to find relevant information from data.

d$.$

to search data

Qity.

d$.$

No