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WorksheetsMachine Learning Internship Quiz
Total questions: 15
Worksheet time: 9mins
Which statement about outliers is true?
outliers should be part of the training dataset but should not be present in the test data.
outliers should be part of the test dataset but should not be present in the training data.
The nature of the problem determines how outliers are used.
outliers should be identified and removed from a dataset.
A regression model in which more than one independent variables in a multiple regression model are correlated is
regression
correlation
multicollinearity
none of the above
A measure of goodness of fit for the estimated regression equation is the
multiple coefficient of determination
mean square of error
mean square due to regression
none of the above
If we decrease the input variable by one unit in a simple linear regression model. How many units of the output variable will change?
reduced by Intercept
increased by Intercept
increased by Slope
reduced by Slope
The correlation in between mobile usage and exam score of a person found to be -2.2. What is your inference from the above statement?
Mobile usage is positively correlated with exam score
Mobile usage is negatively correlated with exam score
None of the mentioned
Need some other information
The correlation coefficient for two attributes is 0.90. What does this value tell you?
the attributes are not linearly related.
as the value of one attribute increases the value of the second attribute also increases.
as the value of one attribute decreases the value of the second attribute increases.
the attributes show a curvilinear relationship.
If two variables, x and y, have a very strong linear relationship, then
there is evidence that xcauses a change in y
there is evidence that ycauses a change in x
there might not be any causal relationship between x and y
None of these alternatives is correct
In a regression analysis if r2(R Squared)= 1, then sum of squares error (SSE)
must also be equal to one
must be equal to zero
can be any positive value
must be negative
The relationship between number of beers consumed (x) and blood alcohol content (y) was studied in 16 male college students by using least squares regression. The following regression equation was obtained from this study:
y= -0.0127+ 0.0180x
The above equation implies that:
each beer consumed increases blood alcohol by 1.27%
on average it takes 1.8 beers to increase blood alcohol content by 1%
each beer consumed increases blood alcohol by an average of amount of 1.8%
each beer consumed increases blood alcohol by exactly 0.018
How do you choose the root node while constructing a Decision Tree?
An attribute having high entropy
An attribute having largest information gain
An attribute having high entropy and Information gain
None of the Mentioned
Which of the following is FALSE about Random Forest and Adaboost?
Random Forest aims to decrease variance and not bias
Adaboost aims to decrease bias not variance
Both Adaboost and Random Forest aim to decrease both bias and variance
None of the above
You’ve just finished training a random forest for spam classification, and it is getting abnormally bad performance on your validation set, but good performance on your training set. Your implementation has no bugs. What could be causing the problem?
Your decision trees are too deep
You are randomly sampling too many features when you choose a split
You have too few trees in your ensemble
all of them
Which of the following techniques can be used for normalization in text mining?
Stemming
Lemmatization
Stop Word Removal
Both A and B
In neural networks, nonlinear activation functions such as sigmoid, tanh, and ReLU
speed up the gradient calculation in backpropagation, as compared to linear units
are applied only to the output units
help to learn nonlinear decision boundaries
always output values between 0 and 1
What does back propagation for neural network training determines?
error not represented by rest of the model
inputs that are mapped to numerical output
weights and residual represented by hidden layers
error added to the each node of the data
