global business testthe person should be familiar with the following topics:World Economy and Its Key PlayersCovid-19 crisis – impact on the global economy and businessGlobal Business EnvironmentForeign Exchange MarketsInternational Trade TheoryGovernment Trade RegulationsExport-Import OperationsInvesting InternationallyOperations of a Multinational FirmGlobal Labor Markets and ImmigrationEmerging Market EconomiesGlobal Economic ForecastsManaging Careers Across Bordersthe test will be 8 essay questions and 10 multiples choices

DSCI 352 Assignment 5 – Predicting BreastCancerIn this assignment you will be applying machine learning to try to predict breast cancer from aset of biological attributes in a tissue sample. Specifically, you will be constructing, evaluating,and comparing three different classification models.As this is an online assignment, it is imperative that you CAREFULLY READ AND FOLLOWALL DIRECTIONS. Failure to do so may result in points lost unnecessarily.This assignment is to be entirely your own work – by submitting you certify that this is true.To begin, please download the following data file:https://drive.google.com/file/d/1JUfmGe_3Tr_3hkM55dV-rAYCBuTizIsX/view?usp=sharingPREREQUISITES – Getting Set Up First, set your working directory to the folder where you downloaded the data file above. Second, add the following two lines to your code file AS THE FIRST TWO LINES (ifyou are using a code file), or else copy them into your R console BEFORE DOINGANYTHING ELSE (if you are working directly in the command line):o library(caret)o set.seed(32343)The first command imports the caret library for machine learning, and the set.seed method willensure that everyone gets the same answer when using randomized methods such as randomforests. YOU MUST MAKE SURE TO DO THESE TWO STEPS BEFORE PROCEEDINGOR YOUR ANSWERS WILL NOT BE CORRECT.

1.
In this assignment we are going to build predictive models which will use cell/tissuesample information to predict whether the sample is normal or or breast cancer.To begin, read in the above data set and use head to inspect the first few rows.Which column(s) represent the dependent variable (i.e. the one we want to predict)?
(1 point)


Patient.ID


Clump.Thickness


Size.Uniformity


Shape.Uniformity


Marginal.Adhesion


Epithelial.Cell.Size


Bare.Nuclei


Bland.Chromatin


Normal.Nuclei


Mitosis


Diagnosis

2.
Looking at the data above, which column(s) represent the independent variable(s)(i.e. the one(s) which may conceivably have a predictive relationship with what weare trying to predict)?
(1 point)


Patient.ID


Clump.Thickness


Size.Uniformity


Shape.Uniformity


Marginal.Adhesion


Epithelial.Cell.Size


Bare.Nuclei


Bland.Chromatin


Normal.Nuclei


Mitosis


Diagnosis

3.
How many benign cases are found in this data? (Enter only a number)*Hint: Use subsetting to find this
(1 point)

4.
How many malignant cases are found in this data? (Enter only a number)
(1 point)

5.
Is this data set balanced?
(1 point)


Yes, it is approximately balanced


No, it is slightly imbalanced (i.e. one class is no more than twice the size of the other)


No, it is moderately imbalanced (one class is between 2 to 5 times the size of the other)


No, it is highly imbalanced (one class is more than 5 times the size of the other)

INSTRUCTIONS: Please carry out the following steps to prepare your data for further analysisbefore proceeding to the next questions:1. Use data slicing to get rid of any columns in the data set which are neither dependent norindependent variables as you specified above.2. Split the data into training and test sets. The training set should contain exactly 70% ofthe data and the test set should contain the remaining 30%.MODEL 1 – Logistic Regression: Construct a logistic regression model to predict thedependent variable identified above from the independent variables you identified (in questions 1and 2 above). DO NOT USE ANY SCALING OR OTHER PREPROCESSING. Train yourmodel on the training data and test/evaluate its performance using the test data. When evaluatingyour results, make sure to set the positive class to ‘Malignant’. You will use the results of thismodel evaluation to answer the next 8 questions.

6.
What is the accuracy of this model? (Enter a number only, no rounding)
(1 point)

7.
What is the precision of this model? (Enter a number only, no rounding)
(1 point)

8.
What is the recall of this model? (Enter a number only, no rounding)
(1 point)

9.
What is the balanced accuracy of this model? (Enter a number only, no rounding)
(1 point)

10.
How many cases in the test data did this model correctly predict as benign? (Enteronly a number, no rounding)
(1 point)

11.
How many cases in the test data did this model correctly predict as malignant?(Enter only a number, no rounding)
(1 point)

12.
How many cases in the test data did this model incorrectly predict as benign (i.e.how many false negatives)? (Enter only a number, no rounding)
(1 point)

13.
How many cases in the test data did this model incorrectly predict as malignant(i.e. how many false positives)? (Enter only a number, no rounding)
(1 point)
MODEL 2 – Naive Bayes: Construct a Naive Bayes model to predict the dependent variableidentified above from the independent variables you identified (in questions 1 and 2 above). DONOT USE ANY SCALING OR OTHER PREPROCESSING. Train your model on thetraining data and test/evaluate its performance using the test data. When evaluating your results,make sure to set the positive class to ‘Malignant’. You will use the results of this modelevaluation to answer the next 8 questions.

14.
What is the accuracy of this model? (Enter a number only, no rounding)
(1 point)

15.
What is the precision of this model? (Enter a number only, no rounding)
(1 point)

16.
What is the recall of this model? (Enter a number only, no rounding)
(1 point)

17.
What is the balanced accuracy of this model? (Enter a number only, no rounding)
(1 point)

18.
How many cases in the test data did this model correctly predict as benign? (Enteronly a number, no rounding)
(1 point)

19.
How many cases in the test data did this model correctly predict as malignant?(Enter only a number, no rounding)
(1 point)

20.
How many cases in the test data did this model incorrectly predict as benign (i.e.how many false negatives)? (Enter only a number, no rounding)
(1 point)

21.
How many cases in the test data did this model incorrectly predict as malignant
(1 point)
(i.e. how many false positives)? (Enter only a number, no rounding)MODEL 3 – Random Forest: Construct a Random Forest model to predict the dependentvariable identified above from the independent variables you identified (in questions 1 and 2above). DO NOT USE ANY SCALING OR OTHER PREPROCESSING. Train your modelon the training data and test/evaluate its performance using the test data. When evaluating yourresults, make sure to set the positive class to ‘Malignant’. You will use the results of this modelevaluation to answer the next 8 questions.

22.
What is the accuracy of this model? (Enter a number only, no rounding)
(1 point)

23.
What is the precision of this model? (Enter a number only, no rounding)
(1 point)

24.
What is the recall of this model? (Enter a number only, no rounding)
(1 point)

25.
What is the balanced accuracy of this model? (Enter a number only, no rounding)
(1 point)

26.
How many cases in the test data did this model correctly predict as benign? (Enteronly a number, no rounding)
(1 point)

27.
How many cases in the test data did this model correctly predict as malignant?(Enter only a number, no rounding)
(1 point)

28.
How many cases in the test data did this model incorrectly predict as benign (i.e.how many false negatives)? (Enter only a number, no rounding)
(1 point)

29.
How many cases in the test data did this model incorrectly predict as malignant(i.e. how many false positives)? (Enter only a number, no rounding)
(1 point)
Model Comparison: Use the results from your three models above to answer the remainingquestions.

30.
Based on the three models you constructed and the results, can we make goodpredictions about a tissue sample being breast cancer or normal?
(1 point)


No, accuracy was below 60% in all models


No, balanced accuracy was below 60% for all models


No, accuracy, precision, recall, and balanced accuracy were below 60% for all models


This depends on the model, in some cases accuracy and precision were below 60%


Yes, accuracy was above 90% in all cases


Yes, accuracy, precision, recall, and balanced accuracy were above 90% in all models.

31.
Suppose we want to select the model with the least chance of missing a case ofbreast cancer (i.e. missing a positive instance). Which metric should we use tocompare models?
(1 point)


Prevalance


Specificity


Detection Prevalence


Sensitivity


Balanced Accuracy


Neg Pred Value


Pos Pred Value


Detection Rate


Kappa

32.
Suppose we want to select the model in which we can have the greatest certaintythat if the predicted outcome is positive (i.e. predicted as malignant), this is in factcorrect. Which metric should we use to compare models?
(1 point)


Sensitivity


Prevalance


Detection Rate


Detection Prevalence


Pos Pred Value


Balanced Accuracy


Kappa


Specificity


Neg Pred Value

33.
By the criteria chosen above, which model would be the worst?
(1 point)


Logistic Regression


Naive Bayes


Random Forest

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