WorksheetsMLIT(Round 1)
Total questions: 15
Worksheet time: 13mins
Name
Class
Date
1.
Machine Learning is a Subset of
a)
Deep Learning
b)
Artificial Intelligence
c)
Data Learning
d)
None of the above
2.
What is machine learning?
a)
To learn from data without being explicitly programmed.
b)
To learn from data by being explicitly programmed.
c)
To learn from data using neuron structure.
d)
To learn from data by only using statistics.
3.
Supervised learning and unsupervised clustering both require at least one .............
a)
Input attribute
b)
Output attribute
c)
Hidden attribute
d)
Categorical attribute
4.
If machine learning model output involves target variable then that model is called as
a)
reinforcement learning
b)
descriptive model
c)
Predictive model
d)
all of the above
5.
What is unsupervised learning?
a)
Number of groups maybe known
b)
Features of group explicitly stated
c)
Neither feature nor number of groups is known
d)
None of the above
6.
Among the following option identify the one which is not a type of learning
a)
Supervised learning
b)
Semi unsupervised learning
c)
Unsupervised learning
d)
Reinforcement learning
7.
What is the application of machine learning methods to a large database called?
a)
Big data computing
b)
Internet of things
c)
Data mining
d)
Artificial intelligence
8.
The father of machine learning is
a)
Geoffrey Hill
b)
Geoffrey Everest Hinton
c)
Geoffrey Chaucer
d)
None of the above
9.
Which of the following are common classes of problems in machine learning?
a)
Regression
b)
Classification
c)
Clustering
d)
All of the above
10.
What are the applications of Machine Learning?
a)
Email spam detection
b)
Fraud Detection
c)
Healthcare diagnosis
d)
All of the above
11.
How much percentage of data is preferred to allocate for training and testing?
a)
Train - 25% Test - 75%
b)
Train - 75% Test - 25%
c)
Train - 50% Test - 50%
d)
Test - 10% Test - 90%
12.
What is one reason not to use the same data for both your training set and your testing set?
a)
You will almost certainly underfit the model.
b)
You will almost certainly overfit the model.
c)
You might not have enough data for both.
d)
You will pick the wrong algorithm.
13.
What is TF-IDF formula (Term Frequency - Inverse Document Frequency)?
a)
TF(t,d) * IDF(t)
b)
TF(t,d) / IDF(t)
c)
TF(t) * IDF(t,d)
d)
TF(t) / IDF(t,d)
14.
What is Logistic Regression is mainly used for?
a)
Classification
b)
Clustering
c)
Regression
d)
All of the above
15.
Which of the following are options of categorical data?
a)
Gender
b)
Bank Balance
c)
House Prices
d)
Height
100 %
