MBA 509 Final Exam Questions & Answers 2024/2025
6 Stages on CRISP-DM - ANSWERSBusiness Understanding
Data Understanding
Data Preparation
Modeling
Evaluation
Deployment Becuase Dumb Drunks Move, Everyone Dies
Business Understanding - ANSWERS-Translate the Business problem into a data m...
6 Stages on CRISP-DM - ANSWERSBusiness Understanding
Data Understanding
Data Preparation
Modeling
Evaluation
Deployment Becuase Dumb Drunks Move, Everyone Dies
Business Understanding - ANSWERS-Translate the Business problem into a data mining problem
-Focuses on understanding the project objectives and requirements from a business perspective, and
then converting this knowledge into a data mining problem definition and a preliminary plan.
Data Understanding - ANSWERS-Select appropriate data
-Get to know the data
-Starts with an initial data collection and proceeds with activities in order to get familiar with the data, to
identify data quality problems, to discover first insights into the data, or to detect interesting subsets to
form hypotheses for hidden information.
Data Preparation - ANSWERS-Create a model set of data
-Fix the problems with the data
-Transform data to bring information to the surface
-The data preparation phase covers all activities to construct the final dataset from the initial raw data.
Modeling - ANSWERS-Build Models
-Modeling techniques are selected and applied. Since some techniques like neural nets have specific
requirements regarding the form of the data, there can be a loop back here to data prep.
, Evaluation - ANSWERS-Assess Models
-Once one or more models have been built that appear to have high quality based on whichever loss
functions have been selected, these need to be tested to ensure they generalize against unseen data and
that all key business issues have been sufficiently considered. The end result is the selection of the
champion model(s).
Deployment - ANSWERS-Deploy Models
-Assess results
-Begin Again
-the deployment phase can be as simple as generating a report or as complex as implementing a
repeatable data mining process across the enterprise.
Sensitivity - ANSWERSTrue Positive Rate = A/(A+C) *More important
Specificity - ANSWERSTrue Negative Rate =D/(B+D)
Overall Accuracy - ANSWERS=A+D/(sum of all squares)
Sensitivity - ANSWERSThe true positive rate. How the class' positive prediction compares to the true
class positive
Specificity - ANSWERSThe true negative rate. How the class' negative prediction compares to the true
class negative
Confusion Matrix - ANSWERSSensitivity, Specificity, Accuracy (Square with A, B, C, D)
Text Analysis Pre-Processing Steps - ANSWERSCorpus, N-Grams, Tokenization
Corpus - ANSWERSall of the text associated with a given problem
N-grams - ANSWERSgrouping a specified number of words together
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