Quiz
Lecture 1 - Intro
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Questions in this quiz (20)
1What is the process to extract and identify useful but unexpected information and subsequent knowledge from large databases?
- AETL processing
- BData mining
- CData mart
- DDatabase
- EData warehousing
2What is the class of data mining and machine learning applications and techniques where the system builds a model based on the prediction of a well defined prediction field (=class variable)?
- AShallow learning
- BSemi-supervised learning
- CSupervised learning
- DUnsupervised learning
- ERandom learning
3What is a class of data mining algorithm that establishes relationships about items that occur together in a transaction record?
- AText mining
- BClustering
- CRegression
- DClassification
- EAssociation rule mining
4What is a data analysis technique classically used in statistics for building predictive models for continuous prediction fields?
- AAssociation rule mining
- BRegression
- CText mining
- DClustering
- EClassification
5What is a class of data mining algorithm to non-structured or less structured text files?
- AClustering
- BAssociation rule mining
- CText mining
- DClassification
- EWeb mining
6What is a set of data analysis techniques that divides a data set so that records with similar content are in the same group, and groups are as different as possible?
- AClustering
- BClassification
- CWeb structure mining
- DStatistical natural language processing
- EAssociation rule mining
7_____ is a tendency of some modeling techniques to assign importance to random variations in the data by declaring them important patterns.
- ASensitivity-specificity theorem
- BBootstrapping
- CCentral limit theorem
- DOverfitting
8____ is a data set used to calibrate data mining models and consists of randomly chosen 2/3 of original data sets.
- ATest data
- BCross-validation
- CTraining data
- DHoldout data
- EHidden data
9In k-fold _____, the data set is divided into k equal parts, with each part in turn used to test a model fitted to the remaining parts.
- AHoldout data
- BCross-validation
- CTraining data
- DTest data
- EHidden data
10_____ shows the counts of the actual vs. predicted class values. It shows not only how well the model predicts, but also presents the details needed to see exactly where things may have gone wrong.
- ADiagonal matrix
- BClustering matrix
- CInverse matrix
- DFirm matrix
- EConfusion matrix
11_____ is the estimate of error based on the difference between the predictions of a model on a test data set and the observed values in the test data set when the test data set was not used to train the model.
- ATrain error
- BValidation error
- CRegression error
- DOutlier error
- ETest error
12_____ measures the proportion of positives that are correctly identified.
- ASensitivity
- BPrecision
- CSpecificity
- DReliability
- EAccuracy
13_____ measures the proportion of positives out of predicted positives.
- AReliability
- BAccuracy
- CSpecificity
- DPrecision
- ESensitivity
14____ graphically represents the improvement that a mining model provides when compared against a random guess, and measures the change in terms of a lift score.
- AProfit chart
- BSensitivity chart
- CClassification chart
- DLift chart
- EROC curve
15_____ displays the performance of a data mining model by showing sensitivity on the y-axis, and false positive rate (=1-specificity) on the x-axis.
- ALift chart
- BROC curve
- CSensitivity chart
- DClassification chart
- EProfit chart
16See image
- A2 or 3
- B1 or 3
- C1 or 2
- D1 or 4
- E3, 4 or 5
17____ displays the estimated profit increase that is associated with using a mining model by incorporating information about the cost of a targeted mailing campaign toward a potential group of customers.
- ALift chart
- BROC curve
- CClassification chart
- DProfit chart
- ESensitivity chart
18Which of the following statements is NOT correct?
- ASampling is the process by which only a fraction of all available data is selected.
- BOutliers are data items that did not come from the assumed population of data
- CTraining is another term for estimating a model's parameters based on the data set at hand
- DBootstrapping is the process of re-sampling without replacement from the original set
- EAll statements are correct
19Which of the following statements is NOT correct?
- AClassification refers to the data mining problem of attempting to predict the category of categorical data by building a model based on some predictor variables
- BCategorical data have a small number of discrete categories
- CCategorical data can have any value in an interval of real numbers
- DClassifier is a data mining model built to predict the class variable
- ECardinality is the number of different values a categorical variable has
20_____ is a rule of thumb used by many scientists that advocates favoring the simplest model/theory that adequately explains (or predicts) an event?
- ABootstrapping
- BCentral limit theorem
- CBayes theorem
- DOccam's razor
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- Description
- 6500 Data Mining Quiz on Lecture 1 - Intro, created by Brieanne Woodbury on 15/02/2021.
- Author
- Brieanne Woodbury
- Language
- English
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