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WorksheetsIVY-TATA STEEL-ML-AUGUST 11
Total questions: 23
Worksheet time: 23mins
Name
Class
Date
1.
What is Machine learning?
a)
The autonomous acquisition of knowledge through the use of manual programs
b)
The selective acquisition of knowledge through the use of computer programs
c)
The autonomous acquisition of knowledge through the use of computer programs
d)
The selective acquisition of knowledge through the use of manual programs
2.
Discovering hidden patterns in any given data is what kind of learning algorithm?
a)
Unsupervised Learning: Clustering
b)
Supervised Learning: Classification
c)
Reinforcement Learning
d)
Unsupervised Learning: Regression
3.
What type of Machine Learning Algorithm is suitable for predicting the dependent variable with two different values?
a)
Logistic Regression
b)
Linear Regression
c)
Multiple Linear Regression
d)
Polynomial Regression
4.
Suitable evaluation metric for measuring the performance of a given regression model is
a)
Root Mean Square Error
b)
Precision
c)
Recall
d)
Both 2 and 3
5.
If we decrease the input variable by one unit in a simple linear regression model. How many units of the output variable will change?
a)
reduced by Intercept
b)
increased by Intercept
c)
increased by Slope
d)
reduced by Slope
6.
Which statement about outliers is true?
a)
outliers should be identified and removed from a dataset.
b)
outliers should be part of the training dataset but should not be present in the test data.
c)
outliers should be part of the test dataset but should not be present in the training data.
d)
The nature of the problem determines how outliers are used.
7.
The correlation coefficient for two attributes is 0.90. What does this value tell you?
a)
the attributes are not linearly related.
b)
as the value of one attribute increases the value of the second attribute also increases.
c)
as the value of one attribute decreases the value of the second attribute increases.
d)
the attributes show a curvilinear relationship.
8.
In a regression analysis if r2(R Squared)= 1, then sum of squares error (SSE)
a)
must also be equal to one
b)
must be equal to zero
c)
can be any positive value
d)
must be negative
9.
A regression model in which more than one independent variables in a multiple regression model are correlated is
a)
regression
b)
correlation
c)
multicollinearity
d)
none of the above
10.
How do you handle missing or corrupted data in a dataset?
a)
Drop missing rows or columns
b)
Replace missing values with mean/median/mode
c)
Assign a unique category to missing values
d)
All of the above
11.
Simple regression is a ............. relationship between 2 or more variables
a)
Linear
b)
Non-Linear
c)
Categorical
d)
Systematical
12.
Machine learning depends on?
a)
how smart the person on the computer is
b)
having a good machine
c)
accessing an ever-growing database of increasingly complex information
d)
All options are correct
13.
Which of the following is a disadvantage of decision trees?
a)
Factor analysis
b)
Decision trees are robust to outliers
c)
Decision trees are prone to be overfit
d)
None of the above
14.
Which of the following is an example of feature extraction?
a)
Constructing bag of words vector from an email
b)
Applying PCA projects to a large high-dimensional data
c)
Removing stop words in a sentence
d)
All of the above
15.
Which statement about outliers is true?
a)
outliers should be identified and removed from a dataset.
b)
outliers should be part of the training dataset but should not be present in the test data.
c)
outliers should be part of the test dataset but should not be present in the training data.
d)
The nature of the problem determines how outliers are used.
16.
Which of the following are true about bagging?
a)
In bagging, we choose one subsamples of the input points with replacement
b)
Bagging is ineffective with logistic regression, because all of the learners learn exactly the same decision boundary
c)
The main purpose of bagging is to decrease the bias of learning algorithms.
d)
If we use decision trees that have one sample point per leaf, bagging never gives lower training error than one ordinary decision tree
17.
What is the supervised machine learning?
a)
given an input and predict the parameters
b)
Prediction of a unlabeled dataset
c)
predict future data based on past data
d)
finds the patterns from the dataset
18.
What is one reason not to use the same data for both your training set and your testing set?
a)
data will underfit the model.
b)
data will choose wrong algorithm.
c)
not have enough data for both.
d)
data will overfit the model.
19.
What type of Machine Learning Algorithm is suitable for predicting the continuous dependent variable?
a)
Logistic Regression
b)
Linear Regression
c)
Decision Tree Classifier
d)
KNN Classifier
20.
The Number of coefficients required to estimate a simple linear regression?
a)
1
b)
2
c)
0
d)
3
21.
the process of making the system able to learn.
a)
Training
b)
Testing
c)
Labelling
d)
Classifying
22.
Which of the following is TRUE about Random Forest?
a)
Random Forest aims to decrease both variance and bias
b)
Random Forest aims to decrease variance not bias
c)
Random Forest aims to decrease bias not variance
d)
None of the above
23.
Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging?
a)
Decision Tree
b)
Regression
c)
Classification
d)
Random Forest
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