Question 1

KNN is a simple data mining tool, known for developing personalized recommendations for many online company applications.

Accepted Answer

A) True 
 B)False

Question 2

When performing a naïve Bayes analysis, all predictor variables must be categorical.

Accepted Answer

A) True 
 B)False

Question 3

To use the naïve Bayes method, numerical variables can be converted into discrete categories, through a process called binning, and then stored in a newly created categorical value.

Accepted Answer

A) True 
 B)False

Question 4

A new applicant, age 32, is applying for a loan. Using the table below, what is the estimated probability the loan will default using k = 3.

Accepted Answer

A)  100% 
B)  33% 
C)  67% 
D)  0% 
A)  100% 
B)  33% 
C)  67% 
D)  0%

Question 5

This chart measures the effectiveness of a predictive model, containing both a baseline and a lift curve.

Accepted Answer

A)  Decile-wise chart 
B)  Cumulative lift chart 
C)  Scatterplot 
D)  ROC Curve 
A)  Decile-wise chart 
B)  Cumulative lift chart 
C)  Scatterplot 
D)  ROC Curve

Question 6

A researcher is preparing data for a k-fold cross-validation. The number of groups the sample data is to be split into is 10. What would k equal in a 10-fold cross-validation?

Accepted Answer

A)  k = 1 
B)  k = 5 
C)  k = 10 
D)  k cannot be determined until applied to a machine model. 
A)  k = 1 
B)  k = 5 
C)  k = 10 
D)  k cannot be determined until applied to a machine model.

Question 7

The naïve Bayes method is an unsupervised data mining technique that uses partitioning to assess model performance.

Accepted Answer

A) True 
 B)False

Question 8

Mark is reviewing a partial summary of results from a test data set on a small health clinic. With an accuracy 84% (100 count), Sensitivity 55%, and Specificity 100%, can Mark correctly predict the true positive rate to identify those with the flu?

Accepted Answer

A)  Yes, because the specificity is 100% identifying healthy patients. 
B)  No, because the test set identified all patients with the flu. 
C)  No, because the sensitivity rate is only 55% in identifying those with the flu. 
D)  Yes, because there is a 84% accuracy rate with all healthy identified. 
A)  Yes, because the specificity is 100% identifying healthy patients. 
B)  No, because the test set identified all patients with the flu. 
C)  No, because the sensitivity rate is only 55% in identifying those with the flu. 
D)  Yes, because there is a 84% accuracy rate with all healthy identified.

Question 9

KNN belongs to a category of mining techniques called computer-based-reasoning.

Accepted Answer

A) True 
 B)False

Question 10

What is the estimated probability that the cheese sample tested in NW will be Gouda? k = 3

Accepted Answer

A)  40% 
B)  60% 
C)  34% 
D)  67% 
A)  40% 
B)  60% 
C)  34% 
D)  67%

Question 11

The following table reflects the observations made on the color and type of vehicle, if a speeding ticket was received (1) or a warning (0), and if there was a prior driving violation (yes or no). Using the naïve Bayes calculation, what is the conditional probability of receiving a ticket with a red vehicle.

Accepted Answer

A)  0.88 
B)  0.53 
C)  0.40 
D)  0.75 
A)  0.88 
B)  0.53 
C)  0.40 
D)  0.75

Question 12

What is the Euclidean distance between Observation 1 and the origin point of (0, 0, 0)?

Accepted Answer

A)  2 
B)  1 
C)  0 
D)  3 
A)  2 
B)  1 
C)  0 
D)  3

Question 13

Binning is a process where categorical data is transformed into numerical segments that can be appended back to the original data set to use a naïve Bayes method.

Accepted Answer

A) True 
 B)False

Question 14

What is the estimated probability that the cheese sample tested in NW will be Gouda? k = 3

Accepted Answer

A)  40% 
B)  60% 
C)  34% 
D)  67% 
A)  40% 
B)  60% 
C)  34% 
D)  67%

KNN is a simple data mining tool, known for developing personalized recommendations for many online company applications.

When performing a naïve Bayes analysis, all predictor variables must be categorical.

To use the naïve Bayes method, numerical variables can be converted into discrete categories, through a process called binning, and then stored in a newly created categorical value.

A new applicant, age 32, is applying for a loan. Using the table below, what is the estimated probability the loan will default using k = 3.

This chart measures the effectiveness of a predictive model, containing both a baseline and a lift curve.

A researcher is preparing data for a k-fold cross-validation. The number of groups the sample data is to be split into is 10. What would k equal in a 10-fold cross-validation?

The naïve Bayes method is an unsupervised data mining technique that uses partitioning to assess model performance.

Mark is reviewing a partial summary of results from a test data set on a small health clinic. With an accuracy 84% (100 count), Sensitivity 55%, and Specificity 100%, can Mark correctly predict the true positive rate to identify those with the flu?

KNN belongs to a category of mining techniques called computer-based-reasoning.

What is the estimated probability that the cheese sample tested in NW will be Gouda? k = 3

What is the Euclidean distance between Observation 1 and the origin point of (0, 0, 0)?

Binning is a process where categorical data is transformed into numerical segments that can be appended back to the original data set to use a naïve Bayes method.

What is the estimated probability that the cheese sample tested in NW will be Gouda? k = 3

Introduction to Business Analytics

Data Management and Wrangling

Data Visualization and Summary Measures

Probability and Probability Distributions

Statistical Inference

Regression Analysis

Advanced Regression Analysis

Introduction to Data Mining

Supervised Data Mining: Decision Trees

Unsupervised Data Mining

Forecasting With Time Series Data

Introduction to Prescriptive Analytics

Filters

Exam 9: Supervised Data Mining: K-Nearest Neighbors and Naãve Bayes

KNN is a simple data mining tool, known for developing personalized recommendations for many online company applications.

When performing a naïve Bayes analysis, all predictor variables must be categorical.

To use the naïve Bayes method, numerical variables can be converted into discrete categories, through a process called binning, and then stored in a newly created categorical value.

A new applicant, age 32, is applying for a loan. Using the table below, what is the estimated probability the loan will default using k = 3.

This chart measures the effectiveness of a predictive model, containing both a baseline and a lift curve.

A researcher is preparing data for a k-fold cross-validation. The number of groups the sample data is to be split into is 10. What would k equal in a 10-fold cross-validation?

The naïve Bayes method is an unsupervised data mining technique that uses partitioning to assess model performance.

Mark is reviewing a partial summary of results from a test data set on a small health clinic. With an accuracy 84% (100 count), Sensitivity 55%, and Specificity 100%, can Mark correctly predict the true positive rate to identify those with the flu?

KNN belongs to a category of mining techniques called computer-based-reasoning.

What is the estimated probability that the cheese sample tested in NW will be Gouda? k = 3

What is the Euclidean distance between Observation 1 and the origin point of (0, 0, 0)?

Binning is a process where categorical data is transformed into numerical segments that can be appended back to the original data set to use a naïve Bayes method.

What is the estimated probability that the cheese sample tested in NW will be Gouda? k = 3

Introduction to Business Analytics

Data Management and Wrangling

Data Visualization and Summary Measures

Probability and Probability Distributions

Statistical Inference

Regression Analysis

Advanced Regression Analysis

Introduction to Data Mining

Supervised Data Mining: Decision Trees

Unsupervised Data Mining

Forecasting With Time Series Data

Introduction to Prescriptive Analytics

Filters