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WorksheetsML WORKSHOP QUIZ
Total questions: 27
Worksheet time: 9mins
Name
Class
Date
1.
Among the following option identify the one which is not a type of learning
a)
Semi Unsupervised Learning
b)
Supervised Learning
c)
Unsupervised Learning
d)
Reinforcement Learning
2.
Identify the kind of learning algorithm for “facial identities for facial expressions”.
a)
Prediction
b)
Recognition Patterns
c)
Recognizing Anomalies
d)
Generating Patterns
3.
Identify the type of learning in which labeled training data is used.
a)
Semi unsupervised learning
b)
Supervised learning
c)
Reinforcement learning
d)
Unsupervised learning
4.
Machine learning is a subset of which of the following.
a)
Artificial Intelligence
b)
Deep Learning
c)
Data Learning
d)
None of these
5.
Application of machine learning is
a)
email filtering
b)
facial recognition
c)
sentiment analysis
d)
All of the above
6.
K-Nearest Neighbors (KNN) is classified as what type of machine learning algorithm?
a)
Instance-based learning
b)
Parametric learning
c)
Non-parametric learning
d)
Model-based learning
7.
Which of the following is not a supervised machine learning algorithm?
a)
K-means
b)
Naïve Bayes
c)
SVM for classification problems
d)
Decision tree
8.
The learner is trying to predict housing prices based on the size of each house. What type of regression is this?
a)
Multivariate Logistic Regression
b)
Logistic Regression
c)
Linear Regression
d)
Multivariate Linear Regression
9.
The learner is trying to predict housing prices based on the size of each house. The variable “size” is
a)
dependent variable
b)
label set variable
c)
independent variable
d)
target variable
10.
The target variable is represented along
a)
Y axis
b)
X axis
c)
Either Y-axis or X-axis, it doesn’t matter
d)
Depends on the dataset
11.
How many variables are required to represent a linear regression model?
a)
3
b)
2
c)
1
d)
4
12.
What kind of algorithm is logistic regression?
a)
Cost function minimization
b)
Ranking
c)
Regression
d)
Classification
13.
Can a cancer detection problem be solved by logistic regression
a)
Sometimes
b)
No
c)
Yes
d)
Depend on Dataset
14.
Which of the following statements is not true about the Random forests?
a)
It is an ensemble learning method for classification only
b)
It operates by constructing a multitude of decision trees at training time
c)
It outputs the class that is the mode of the classes
d)
It outputs the mean prediction of the individual trees
15.
Support Vector Machine (SVM) can be used for
a)
classification only
b)
regression only
c)
classification and regression both
d)
None of these
16.
In SVM, if the number of input features is 2, then the hyperplane is a .
a)
Line
b)
Circle
c)
Plane
d)
None of these
17.
Which of the following statements is false about k-Nearest Neighbor algorithm?
a)
It stores all available cases and classifies new cases based on a similarity measure
b)
It has been used in statistical estimation and pattern recognition
c)
It cannot be used for regression
d)
The input consists of the k closest training examples in the feature space
18.
Which of the following is a goal of clustering algorithms?
a)
Classification
b)
Regression
c)
Dimensionality reduction
d)
Grouping similar data points together
19.
K-Means Clustering comes under
a)
Supervised learning Algorithm
b)
Unsupervised Learning Algorithm
c)
Reinforcement Learning
d)
None of the above
20.
What is inertia in clustering
a)
Sum of all intra cluster distances
b)
Sum of all inter cluster distances
c)
Sum of distances between centroids
d)
None of the above
21.
In simple term what is machine learning
a)
training based on historical data
b)
prediction to answer a query
c)
both a and b
d)
automisation of complex task
22.
which of the following is the best machine learning method
a)
scalable
b)
accuracy
c)
fast
d)
all of the above
23.
Applications of machine learning methods to large databases is called
a)
Data Mining
b)
Artificial Intelligence
c)
Big Data Computing
d)
Internet of Things
24.
which of the following is not machine learning
a)
Artificial Intelligence
b)
rule based inference
c)
both a and b
d)
none of the above mentioned
25.
Which of the following is an example of a classification problem?
a)
Predicting the price of a house based on its features
b)
Predicting the weight of a person based on their height
c)
Predicting whether a customer will churn or not
d)
Predicting the age of a person based on their income
26.
Which of the following is an example of a clustering algorithm?
a)
Linear regression
b)
Logistic regression
c)
K-means
d)
Support vector machine
27.
What is the term used to describe the process through which machine learning algorithms create models using sample data?
a)
Training
b)
Learning
c)
Modeling
d)
Prediction
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