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Worksheets[MLforDS] Quiz1
Total questions: 15
Worksheet time: 23mins
In linear regression, we try to ________ the LEAST SQUARE ERRORS of the model to identify the line of best fit
change
maximize
minimize
None of those
Which of these variables is a categorical variable?
Heart rate
Blood type
Weight
A common command to start the training process in various ML libraries is:
Which of these code snippets is correct based on this lesson, if you want to check for the presence of missing values in your dataset? Suppose the dataset is stored in a variable named 'dataset' which is a Pandas DataFrame object.
dataset.isnull().sum()
findMissing(dataset)
sum(null(dataset))
Suppose that we have N independent variables (X1,X2… Xn) and dependent variable is Y.
Now imagine that you are applying linear regression by fitting the best fit line using least square error on this data.
You found that the correlation coefficient for one of its variable (X1) with Y is -0.95.
Which of the following is true for X1?
Relation between the X1 and Y is weak
Correlation can’t judge the relationship
Relation between the X1 and Y is neutral
Relation between the X1 and Y is strong
Which of the following offsets, do we use in linear regression’s least square line fit? Suppose horizontal axis is independent variable and vertical axis is dependent variable.
Vertical offset
None of those
Both, depending on the situation
Perpendicular offset
Overfitting is more likely when you have huge amount of data to train?
TRUE
FALSE
There are five basic steps when you’re implementing linear regression:
a. Check the results of model fitting to know whether the model is satisfactory.
b. Provide data to work with, and eventually do appropriate transformations.
c. Apply the model for predictions.
d. Import the packages and classes that you need.
e. Create a regression model and fit it with existing data.
However, those steps are currently listed in the wrong order. What’s the correct order?
d, b, e, a, c
d, e, c, b, a
e, d, b, a, c
e, c, a, b, d
You work for an insurance company. Which machine learning project would add the most value for the company?
Create an artificial neural network that would host the company directory.
Create an algorithm that consolidates all of your Excel spreadsheets into one data lake.
Use machine learning and big data to research salary requirements.
Use machine learning to better predict risk.
Which of these Python modules is used to plot the visualization of data?
Numpy
Scikit-learn
Matplotlib
Pandas
Which of the following statements is true about outliers in Linear regression?
Linear regression is sensitive to outliers
Can’t say
Linear regression is not sensitive to outliers
Check out these four linear regression plots:
Which one represents an underfitted model?
The top-left plot
The top-right plot
The bottom-left plot
The bottom-right plot
Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider?
(1) Add more variables.
(2) Start introducing polynomial degree variables.
(3) Remove some variables.
1 and 2
2 and 3
1 and 3
1, 2 and 3
θ0=0.5, θ1=0
θ0=0.5, θ1=0.5
θ0=1, θ1=0.5
θ0=0, θ1=0.5
θ0=1, θ1=1
Graphs (A,B, C left to right) show the cost function and Number of iterations. Suppose α1, α2 and α3 are the three learning rates for A, B, C respectively. Which of the following is true about α1, α2 and α3?
α2 < α1 < α3
None of these
α1 = α2 = α3
α1 > α2 > α3
