Concept explainers
a.
Explanation of Solution
Given: The insurance records are evaluated to build the predicting model for the fraudulent claims. Only 1% is considered as fraudulent on the basis of the historic data.
The sample, n, which is applied, is 800. It classifies the values 310 and 270 as frauds and non-frauds, respectively. It misses 90 as frauds, where 130 records are found incorrect which are marked as fraud...
b.
Explanation of Solution
Given: The insurance records are evaluated to build the predicting model for the fraudulent claims. Only 1% is considered as fraudulent on the basis of the historic data.
The sample, n, which is applied, is 800. It classifies the values 310 and 270 as frauds and non-frauds, respectively. It misses 90 as frauds, where 130 records are found incorrect which are marked as fraud.
To find:Â The adjusted misclassification from the record of the predicating model.
Solution:
By analyzing the records from the classification matrix,
Predicted records of fraudulent without any record of non-fraudulent=310-90=220...
c.
Explanation of Solution
Given: The insurance records are evaluated to build the predicting model for the fraudulent claims. Only 1% is considered as fraudulent on the basis of the historic data.
The sample, n, which is applied, is 800. It classifies the values 310 and 270 as frauds and non-frauds, respectively. It misses 90 as frauds, where 130 records are found incorrect which are marked as fraud...
Want to see the full answer?
Check out a sample textbook solution
Chapter 5 Solutions
Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
- Find the dissimilarity matrix of the following dataset: Student Test-1 (nominal) Test-2 (nominal) Test-3 (ordinal) Test-4 (numeric) 1 Code-A Code - I Excellent 80 Code-B Code - I Fail 20 3 Code-C Code - II Fail 100 4 Code-A Code - II Pass 60arrow_forwardThe advantages of transitioning from stepwise regression to all-subsets regression are described in detail below.arrow_forwardA high bias model has a high unpredictable error, while a high variance model has a high systematic error. Select: True or False?arrow_forward
- Build A Machine Learning Regression Model with Python and TensorFlow for : • One Variable • Multiple inputs : A DNN regression • One variable • Full modelarrow_forwardWhat are the major differences in use-cases for linear regression as compared with non-linear types?arrow_forwardIn a database describing 100 examples of printer failures, 75 are hardware failures and 25 are driver failures. Of the hardware failures, 15 had Windows. Of the driver failures, 15 had Windows. If the probability of a driver failure is 25/100, the probability that the system where a failure occurred was Windows is 30/100 and the probability of a failure in the Windows system given it has been caused by the driver is 15/25, what is the probability that a failure has been caused by a driver knowing that the system is Windows?arrow_forward
- Give two examples of unstructured data. Answer: An exam evaluator has randomly selected ten answer papers from a bundle of 100 student submissions. 9 out of these 10 students scored above 80 in their exam. He concludes that most of the students should have done well in their exams as well. What kind of statistical modelling has been done here? Answer:arrow_forwardExplain why all-subsets regression is preferable than stepwise regression.arrow_forwardExplain why you should use all-subsets regression instead of stepwise regression.arrow_forward
- Explain what autocorrelation indicates . What are the main problems that autocorrelation creates for OLS estimation results ? Give two ways to detect autocorrelation problem and the hypothesis that are tested ?arrow_forwardExplain how Logistic Regression works. Note: Please do it with your own words. Thankyouarrow_forwardThe data mining technique involved in predicting a categorical response is called as. A. Regression B. Classification C. Clustering D. Summarizationarrow_forward
Database System ConceptsComputer ScienceISBN:9780078022159Author:Abraham Silberschatz Professor, Henry F. Korth, S. SudarshanPublisher:McGraw-Hill Education
Starting Out with Python (4th Edition)Computer ScienceISBN:9780134444321Author:Tony GaddisPublisher:PEARSON
Digital Fundamentals (11th Edition)Computer ScienceISBN:9780132737968Author:Thomas L. FloydPublisher:PEARSON
C How to Program (8th Edition)Computer ScienceISBN:9780133976892Author:Paul J. Deitel, Harvey DeitelPublisher:PEARSON
Database Systems: Design, Implementation, & Manag...Computer ScienceISBN:9781337627900Author:Carlos Coronel, Steven MorrisPublisher:Cengage Learning
Programmable Logic ControllersComputer ScienceISBN:9780073373843Author:Frank D. PetruzellaPublisher:McGraw-Hill Education