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Projecting a multi-dimensional dataset onto which vector has the greatest variance?
A. first principal component B. first eigenvector C. not enough information given to answer D. second eigenvector E. second principal component
Answer: A Explanation: The method based on principal component analysis (PCA) evaluates the features according to the projection of the largest eigenvector of the correlation matrix on the initial dimensions, the method based on Fisher's linear discriminant analysis evaluates.
Them according to the magnitude of the components of the discriminant vector.
The first principal component corresponds to the greatest variance in the data, by
definition. If we project the data onto the first principal component line, the data is more
spread out (higher variance) than if projected onto any other line, including other principal
components.
Question # 2
You are creating a Classification process where input is the income, education and current
debt of a customer, what could be the possible output of this process.
A. Probability of the customer default on loan repayment B. Percentage of the customer loan repayment capability C. Percentage of the customer should be given loan or not D. The output might be a risk class, such as "good", "acceptable", "average", or "unacceptable".
Answer: D
Explanation: Classification is the process of using several inputs to produce one or more
outputs. For example the input might be the income, education and current debt of a
customer The output might be a risk class, such as "good", "acceptable", "average", or
"unacceptable". Contrast this to regression where the output is a number not a class.
Question # 3
Suppose you have been given two Random Variables X and Y, whose joint distribution is
already known, the marginal distribution of X is simply the probability distribution of X
averaging over information about Y. It is the probability distribution of X when the value of Y
is not known. So how do you calculate the marginal distribution of X
A. This is typically calculated by summing the joint probability distribution over Y. B. This is typically calculated by integrating the joint probability distribution over Y C. This is typically calculated by summing (In case of discrete variable) the joint probability distribution over Y D. This is typically calculated by integrating(ln case of continuous variable) the joint probability distribution over Y.
Answer: A,B,C,D Explanation: Given two random variables X and Y whose joint distribution is known, the
marginal distribution of X is simply the probability distribution of X averaging over
information about Y. It is the probability distribution of X when the value of Y is not known.
This is typically calculated by summing or integrating the joint probability distribution over
Y. '
For discrete random variables, the marginal probability mass function can be written as
Pr(X = x). This is
Description automatically generated
Question # 4
What are the advantages of the mutual information over the Pearson correlation for text
classification problems?
A. The mutual information has a meaningful test for statistical significance. B. The mutual information can signal non-linear relationships between the dependent and independent variables. C. The mutual information is easier to parallelize. D. The mutual information doesn't assume that the variables are normally distributed.
Answer: C
Explanation: A linear scaling of the input variables (that may be caused by a change of
units for the measurements) is sufficient to modify the PCA results. Feature selection
methods that are sufficient for simple distributions of the patterns belonging to different
classes can fail in classification tasks with complex decision boundaries. In addition,
methods based on a linear dependence (like the correlation) cannot take care of arbitrary
relations between the pattern coordinates and the different classes. On the contrary, the
mutual information can measure arbitrary relations between variables and it does not
depend on transformations acting on the different variables. This item concerns itself with feature selection for a text classification problem and
references mutual information criteria. Mutual information is a bit more sophisticated than
just selecting based on the simple correlation of two numbers because it can detect nonlinear relationships that will not be identified by the correlation. Whenever possible: mutual
information is a better feature selection technique than correlation.
Mutual information is a quantification of the dependency between random variables. It is
sometimes contrasted with linear correlation since mutual information captures nonlinear
dependence.
Correlation analysis provides a quantitative means of measuring the strength of a linear
relationship between two vectors of data. Mutual information is essentially the measure of
how much "knowledge" one can gain of a certain variable by knowing the value of another
variable.
Question # 5
In which of the scenario you can use the linear regression model?
A. Predicting Home Price based on the location and house area B. Predicting demand of the goods and services based on the weather C. Predicting tumor size reduction based on input as number of radiation treatment D. Predicting sales of the text book based on the number of students in state
Answer: A,B,C,D
Explanation: Explanation : You can use the linear regression model for predicting the
continuous
output variable based on the input variables. In all the cases mentioned in the
question option, you can see that output
can be predicted based on the input variable.
Option-A: Input: Location, House Area and Output: House Price
Option-B : Input: Weather condition, Output: Demand for the goods and services
Option-C : Input: Number of Radiation Session Output: Tumor Size Reduction
Option-D : Input: Number of students and Output: Sale quantity of text book
Question # 6
Digit recognition, is an example of.....
A. Classification B. Clustering C. Unsupervised learning D. None of the above
Answer: A
Explanation: Supervised learning is fairly common in classification problems because the
goal is often to get the computer to learn a classification system that we have created. Digit
recognition: once again, is a common example of classification learning. More generally,
classification learning is appropriate for any problem where deducing a classification is
useful and the classification is easy to determine. In some cases, it might not even be
necessary to give pre-determined classifications to every instance of a problem if the agent
can work out the classifications for itself. This would be an example of unsupervised
learning in a classification context.
Question # 7
Select the sequence of the developing machine learning applications
A) Analyze the input data
B) Prepare the input data
C) Collect data D) Train the algorithm
E) Test the algorithm
F) Use It
A. A, B, C, D, E, F B. C, B, A, D, E, F C. C, A, B, D, E, F D. C, B, A, D, E, F
Answer: D
Explanation: 1 Collect data. You could collect the samples by scraping a website and
extracting data: or you could get information from an RSS feed or an API. You could have a
device collect wind speed measurements and send them to you, or blood glucose levels, or
anything you can measure. The number of options is endless. To save some time and
effort you could use publicly available data
2 Prepare the input data. Once you have this data, you need to make sure it's in a useable
format. The format we'll be using in this book is the Python list. We'll talk about Python
more in a little bit, and lists are reviewed in appendix A. The benefit of having this standard
format is that you can mix and match algorithms and data sources. You may need to do
some algorithm-specific formatting here. Some algorithms need features in a special
format, some algorithms can deal with target variables and features as strings, and some
need them to be integers. We'll get to this later but the algorithm-specific formatting is
usually trivial compared to collecting data.
3 Analyze the input data. This is looking at the data from the previous task. This could be
as simple as looking at the data you've parsed in a text editor to make sure steps 1 and 2
are actually working and you don't have a bunch of empty values. You can also look at the
data to see if you can recognize any patterns or if there's anything obvious^ such as a few
data points that are vastly different from the rest of the set. Plotting data in one: two, or
three dimensions can also help. But most of the time you'll have more than three features,
and you can't easily plot the data across all features at one time. You could, however use
some advanced methods we'll talk about later to distill multiple dimensions down to two or
three so you can visualize the data.
4 If you're working with a production system and you know what the data should look like,
or you trust its source: you can skip this step. This step takes human involvement, and for
an automated system you don't want human involvement. The value of this step is that it
makes you understand you don't have garbage coming in.
5 Train the algorithm. This is where the machine learning takes place. This step and the
next step are where the "core" algorithms lie, depending on the algorithm.You feed the
algorithm good clean data from the first two steps andextract knowledge or information. This knowledge you often store in a formatthat's readily useable by a machine for the next
two steps.In the case of unsupervised learning, there's no training step because youdon't
have a target value. Everything is used in the next step.
6 Test the algorithm. This is where the information learned in the previous step isput to use.
When you're evaluating an algorithm, you'll test it to see how well itdoes. In the case of
supervised learning, you have some known values you can use to evaluate the algorithm.
In unsupervised learning, you may have to use some other metrics to evaluate the
success. In either case, if you're not satisfied, you can go back to step 4, change some
things, and try testing again. Often thecollection or preparation of the data may have been
the problem, and you'll have to go back to step 1.
7 Use it. Here you make a real program to do some task, and once again you see if all the
previous steps worked as you expected. You might encounter some new data and have to
revisit steps 1-5.
Question # 8
Google Adwords studies the number of men, and women, clicking the advertisement on
search
engine during the midnight for an hour each day.
Google find that the number of men that click can be modeled as a random variable with
distribution
Poisson(X), and likewise the number of women that click as Poisson(Y).
What is likely to be the best model of the total number of advertisement clicks during the
midnight for an hour ?
A. Binomial(X+Y,X+Y) B. Poisson(X/Y) C. Normal(X+Y(M+Y)1/2) D. Poisson(X+Y)
Answer: D
Explanation: The total number of clicks is the sum of the number of men and
women. The sum of two Poisson random variables also follows a Poisson distribution with
rate equal to the sum of their rates.
The Normal and Binomial distribution can approximate the Poisson distribution in
certain cases, but the expressions above do not approximate Poisson(X+Y).
Question # 9
Which of the below best describe the Principal component analysis
A. Dimensionality reduction B. Collaborative filtering C. Classification D. Regression E. Clustering
Answer: A
Question # 10
What are the key outcomes of the successful analytical projects?
A. Code of the model B. Technical specifications C. Presentations for the Analysts D. Presentation for Project Sponsors
Answer: A,B,C,D Explanation: When your analytical project successfully completed they come up with the
following at the end of the projects. Presentations- You will be having presentations like for
the all the stakeholders, generally these presentation will help seniors executives to make
better decisions. Similarly you would be creating presentations for the other teams like
analysts various visuals you would be creating like ROC Curves, Heat Maps, and Bar
Charts etc.
Whatever tools you have used like SAS, R, or Python then accordingly code was
developed and you will get that code as one of the outcome. Also you would have created
a technical specifications for implementing the codes.
