CAP Model Building

Question 1

Gain

Answer 1

Cumulative expected response using predictive model over expected response using random selection

Question 2

Non-parametric modeling

Answer 2

A system where no set of factors can fully describe the system’s performance independent of observed data

Question 3

Diagnostic modeling

Answer 3

A way to analyze data to identify business needs

Question 4

iterative (agile) method

Answer 4

Method under which requirements and solutions evolve through the collaborative effort of self-organizing and cross-functional teams and their customer(s)/end user(s)

Question 5

Prescriptive modeling

Answer 5

A way to analyze data to determine the best way to proceed in the future

Question 6

Model

Answer 6

an abstraction that emphasizes certain aspects of reality to assess or understand the behavior of a system under study; the system may be physical, logical, mathematical, or some other representation of reality, such as an enterprise or some portion of one.

Question 7

Model reliability

Answer 7

Ability of a model to produce consistent results

Question 8

Parallel method

Answer 8

Method in which there is a need for separate development paths to diverge from a common starting point so that there is two or more concurrent “latest” configurations

Question 9

Confusion matrix

Answer 9

A table with two rows and two columns that reports the number of false positives, false negatives, true positives, and true negatives

Question 10

Proprietary code

Answer 10

Non-free computer software for which the software’s publisher or another person retains intellectual property rights—usually copyright of the source code, but sometimes patent rights

Question 11

Automated modeling

Answer 11

Where a computer develops a representation of a process without the user describing the nature of the observed data

Question 12

Steps in building predictive model

Answer 12

1) Understand the problem and data 2) Explore and clean the data 3) Feature extraction and/or selection 4) Model evaluation and selection 5) Model optimization 6) Interpretation of results and predictions

Question 13

Predictive modeling

Answer 13

A way to use factors to forecast outcomes of events

Question 14

Deterministic modeling

Answer 14

A descriptive or predictive model whose performance can be described without any random variation

Question 15

Model extensibility

Answer 15

Ability of a model to be adapted to varying operation modes

Question 16

Bias-variance trade-off

Answer 16

Use most features possible to reduce bias while using fewest features possible to reduce variance

Question 17

Personally Identifiable Information (PII)

Answer 17

Any information relating to a specific identifiable person

Question 18

Document and communicate findings

Answer 18

Tailor message to audience; avoid message distortion to non-technical audiences; use graphics to simplify results and uncover patterns

Question 19

Model scalability

Answer 19

Ability of a model to perform well under different magnitudes of data volume

Question 20

Waterfall method

Answer 20

Method in which the systems development life cycle tasks occur sequentially, with one activity starting only after the previous one has been completed.

Question 21

Model Fidelity

Answer 21

the degree to which a model or simulation reproduces the state and behaviour of a real world object, feature or condition; encourages parsimony of parameters

Question 22

Model stability

Answer 22

a notion in computational learning theory of how a machine learning algorithm is perturbed by small changes to its inputs

Question 23

Conway’s Law

Answer 23

Organizations which design systems are constrained to produce designs which are copies of the communication structures of those organizations. SE management should facilitate communications, streamline controls, and simplify paperwork.

Question 24

Receiver Operating Characteristic (ROC)

Answer 24

Method to assess classifier model by comparing true positive rate to false positive rate as classifier’s discrimination threshold is varied

Question 25

Tips for communication

Answer 25

Describe not just what you did, but why you did it, how the steps are connected, and what it all means.

Question 26

Supervised modeling

Answer 26

Building a representation of a process with user input describing the groups of different observations or records

Question 27

Parametric modeling

Answer 27

A way to represent a system where a finite set of factors describe the system’s performance independent of observed data

Question 28

Descriptive modeling

Answer 28

A way to describe real world events and the relationships between factors responsible for them

Question 29

Multiple use modeling

Answer 29

A representation of events that can be applied to more than one case study

Question 30

Coefficient of determination

Answer 30

R^2 = 1-(SS_ret/SS_tot), how much of variation in the data is explained by the model

Question 31

Interpreability

Answer 31

Ease of an analyst describing decision making method of model to stakeholders

Question 32

Sensitivity analysis uses

Answer 32

Testing the model for validity or accuracy; searching for errors in the model; simplifying the model; calibrating the model; coping with poor or missing data; prioritizing acquisition of information

Question 33

Lift

Answer 33

Expected response of an interval of data selected using predictive model over expected response of interval selected using random selection

Question 34

Static (snapshot) modeling

Answer 34

Modeling a system for purpose of evaluating performance at a specific point in time

Question 35

Stochastic modeling

Answer 35

a descriptive or predictive probability model yielding a location or time sequence representing the state of a system that is subject to random variation

Question 36

Dynamic (movie) modeling

Answer 36

Modeling a system for purpose of evaluating performance change over time