ISYE 6501 Test Questions with Verified
Correct Answers
What is modeling?
Describing a real life situation mathematically
Model
Mathematical approaches to solving analytics problems
1. Regression for ex. can be called the model of choice.
2. If more detail (descriptive and numerical), the use of, for ex. Regression, can be called the
model.
3. The actual values and output along with 1 and 2 can be referred to as the model.
Cross Cutting Concepts
Preparing data, measuring the quality of a model's output, missing data, etc..
Classification
The process of grouping things based on their similarities
Ex. Classifying consumers by who is likely to pay back a loan and who is not
Soft Classifier
A soft classifier is used when it is not possible to completely separate two categories of data
points via the classifying line.
the soft classifier gives as good of a separation as possible rather than a hard classifier that
separates perfectly.
Classifier Rules
, Must define the best separator. Misclassifying a data point may be more detrimental than
misclassifying another. the more costly one bad decision is, the more we want to move away
from it.
Its okay to push the classifier in one direction so that it is conservative if stakes are higher.
Data Point
Every Row, a single observation of information
Attribute/Feature/Covariate/Predictor
Piece of information, column of data
Responses/Outcomes
Special type of column known as being the answer for each data point
Structured Data
Can be easily described and stored
Unstructured Data
Usually in form of text, cannot be easily described or stored
Quantitative Data
Most numerical data, numbers have a meaning
Non Quantitative Data
Descriptive data, Categorical data, any data without numerical meaning ex. zip codes
Categorical Data
numerical data that does not have quantitative meaning
Correct Answers
What is modeling?
Describing a real life situation mathematically
Model
Mathematical approaches to solving analytics problems
1. Regression for ex. can be called the model of choice.
2. If more detail (descriptive and numerical), the use of, for ex. Regression, can be called the
model.
3. The actual values and output along with 1 and 2 can be referred to as the model.
Cross Cutting Concepts
Preparing data, measuring the quality of a model's output, missing data, etc..
Classification
The process of grouping things based on their similarities
Ex. Classifying consumers by who is likely to pay back a loan and who is not
Soft Classifier
A soft classifier is used when it is not possible to completely separate two categories of data
points via the classifying line.
the soft classifier gives as good of a separation as possible rather than a hard classifier that
separates perfectly.
Classifier Rules
, Must define the best separator. Misclassifying a data point may be more detrimental than
misclassifying another. the more costly one bad decision is, the more we want to move away
from it.
Its okay to push the classifier in one direction so that it is conservative if stakes are higher.
Data Point
Every Row, a single observation of information
Attribute/Feature/Covariate/Predictor
Piece of information, column of data
Responses/Outcomes
Special type of column known as being the answer for each data point
Structured Data
Can be easily described and stored
Unstructured Data
Usually in form of text, cannot be easily described or stored
Quantitative Data
Most numerical data, numbers have a meaning
Non Quantitative Data
Descriptive data, Categorical data, any data without numerical meaning ex. zip codes
Categorical Data
numerical data that does not have quantitative meaning
