Practice Machine Learning Multiple Choice Questions with Answers Quiz-2

Q.1.

Machine Learning has various search/ optimization algorithms, which of the following is not evolutionary computation?

Perceptron
Genetic Algorithm (GA)
Neuro Evolution
Genetic Programming (GP)

Q.2.

You are given reviews of movies marked as positive, negative, and neutral. Classifying reviews of a new movie is an example of

Supervised Learning
Unsupervised Learning
Reinforcement Learning
None of these

Q.3.

ML is a field of AI consisting of learning algorithms that?

Improve their performance
At executing some task
Over time with experience
All of the above

Q.4.

Targeted marketing, Recommended Systems, and Customer Segmentation are applications in

Unsupervised Learning: Clustering
Supervised Learning: Classification
Reinforcement Learning
Unsupervised Learning: Regression

Q.5.

Several sets of data related to each other used to make decisions in machine learning algorithms

unsupervised learning
Classifiers
supervised learning
Dataset

Q.6.

When would you reduce dimensions in your data?

When data comes from sensor
When you are using a Linux machine
When your data set is larger than 500GB
When you have larger set of features with similar characteristics

Q.7.

How many types of machine learning?

4
3
2
1

Q.8.

Which feature selection technique uses shrinkage estimators to remove redundant features from data?

Stepwise regression
Sequential feature selection
Neighborhood component selection
Regularization

Q.9.

In a Decision Tree Leaf Node represents_____________

One of the Class Label
One of the complete observation
One of the attribute
None of the Mentioned

Q.10.

High entropy means that the partitions in classification are

pure
not pure
useful
useless

Q.11.

What kind of table compares classifications predicted by the model with the actual class labels?

Chaos table
Confusion Matrix
Prediction plot
Residual plot

Q.12.

_________is the task of approximating a mapping function (f) from input variables (X) to a continuous output variable (Y).

Classification
Regression
Clustering
Decision Tree

Q.13.

What would make a robot intelligent?

It responds to the environment.
It responds to the environment according to previous experiences.
It calculates mathematical problems faster than human minds.
It can jump 1.5 meters higher than humans.

Q.14.

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

Normalize the data -> PCA -> training
PCA -> normalize PCA output -> training
Normalize the data -> PCA -> normalize PCA output -> training
None of the above

Q.15.

Machine Learning has various function representation, which of the following is not numerical functions?

Linear Regression
Support Vector Machines
Neural Network
Case-based

Q.16.

Agglomerative clustering follows ________ .

model based clustering
top down approach.
bottom up approach
partitional clustering

Q.17.

___________ is a type of supervised learning where a target feature, which is of categorical type.

Regression
Labelling
Classification
None

Q.18.

All data is labeled and the algorithms learn to predict the output from the input data

Dataset
Classifiers
supervised learning
unsupervised learning

Q.19.

Machine learning depends on?

how smart the person on the computer is
having a good machine
accessing an ever-growing database of increasingly complex information
All option correct

Q.20.

How to choose the value of K in KNN?

Take the square root of the total data point available in the dataset.
Take the mean of the total data point available in the dataset.
Take the variance of the total data point available in the dataset.
Take the standard deviation of the total data point available in the dataset.

Support mcqgeeks.com by disabling your adblocker.