4/15/26, 6:08 PM ISYE 6501 - Quiz 1 Flashcards | Quizlet
ISYE 6501 - Quiz 1
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Terms in this set (124)
Classification: What is it? What are Classification groups things into discrete
the types? categories.
The types of classification are hard classification
and soft classification.
Hard Classification vs Soft Hard: 100% separation between groups
Classification Soft: minimize misclassifications where possible
Margin vs Accuracy Trade Off A small margin for error reduces your chances of
misclassifying known data points but increases your
chances of misclassifying unknown data points.
A large margin for error increases your chances of
misclassifying known data points but decreases
your chances of misclassifying unknown data
points.
Making trade off decisions is based on what the
cost is for a false-positive and false-negative in a
given business problem.
https://quizlet.com/727988684/isye-6501-quiz-1-flash-cards/ 1/27
,4/15/26, 6:08 PM ISYE 6501 - Quiz 1 Flashcards | Quizlet
Support Vector Machine (SVM): How SVM a classifier that draws a line that best
do they work and what are their separates groups of data points. It does this by
objectives? creating two outer lines that represent the margin
for error and a middle line that is the classifier.
Objectives:
1. All data points are categorized correctly
(accuracy)
2. Maximize the gap between the two outer lines
(margin)
SVM Hard Classifier: What are the "Subject to all points being accurate, we seek to
parts and what do they represent? maximize margin of error"
The sum of coefficents x variables x y must be
greater than or equal to 1 in order order to maintain
accuracy. y is the response variable (-1 or 1), so the
classifier must produce a prediction that is the
same sign as the response variable in order for the
equation to be true. This part of the equation
ensure accuracy.
We seek to minimize the sum of coefficients
squared since that is the denominator of the margin
calculation (2 / sqrt(sum of coefficents)^2).
Therefore, minimizing the sum of coefficients
squared maximizes the margin of our equation. This
part of the equation maximizes margin.
https://quizlet.com/727988684/isye-6501-quiz-1-flash-cards/ 2/27
, 4/15/26, 6:08 PM ISYE 6501 - Quiz 1 Flashcards | Quizlet
SVM Soft Classifier: What are the "Minimize the sum of total error and square
parts and what do they represent? coefficients" We seek to minimize both in order to
minimize error while maximizing margin.
Max(0 , 1 - the sum of coefficients x variables x y) is
the portion of the equation that measures error.
Any misclassifications will produce a sum of
coefficients x variables x y value that is negative
which will be added to the total error. This part of
the equation ensure accuracy.
Like in hard classification, we seek to minimize the
sum of coefficients squared in order to maximize
margin. This part of the equation maximizes margin.
Unlike hard classification, we have the variable
lambda that is multiplied with margin which can act
as a "lever" for what we want to prioritize.
Lambda in SVM Soft Classifier Lambda is the parameter we can set to control the
trade off between margin and accuracy.
Increasing lambda increases the emphasis the
model has on margin.
Decreasing lambda increases the emphasis the
model has on accuracy.
How do you add business context to You can add the cost of a misclassification to your
build the optimal SVM? model by weighting the a0 variable (intercept).
Ex. If a bad loan (-1) is twice as costly as a good
loan (1) you can weight the a0 variable as 2/3*(a0-1)
+ 1/3(a0+1)
https://quizlet.com/727988684/isye-6501-quiz-1-flash-cards/ 3/27
ISYE 6501 - Quiz 1
5.0 (1 review)
Save
Terms in this set (124)
Classification: What is it? What are Classification groups things into discrete
the types? categories.
The types of classification are hard classification
and soft classification.
Hard Classification vs Soft Hard: 100% separation between groups
Classification Soft: minimize misclassifications where possible
Margin vs Accuracy Trade Off A small margin for error reduces your chances of
misclassifying known data points but increases your
chances of misclassifying unknown data points.
A large margin for error increases your chances of
misclassifying known data points but decreases
your chances of misclassifying unknown data
points.
Making trade off decisions is based on what the
cost is for a false-positive and false-negative in a
given business problem.
https://quizlet.com/727988684/isye-6501-quiz-1-flash-cards/ 1/27
,4/15/26, 6:08 PM ISYE 6501 - Quiz 1 Flashcards | Quizlet
Support Vector Machine (SVM): How SVM a classifier that draws a line that best
do they work and what are their separates groups of data points. It does this by
objectives? creating two outer lines that represent the margin
for error and a middle line that is the classifier.
Objectives:
1. All data points are categorized correctly
(accuracy)
2. Maximize the gap between the two outer lines
(margin)
SVM Hard Classifier: What are the "Subject to all points being accurate, we seek to
parts and what do they represent? maximize margin of error"
The sum of coefficents x variables x y must be
greater than or equal to 1 in order order to maintain
accuracy. y is the response variable (-1 or 1), so the
classifier must produce a prediction that is the
same sign as the response variable in order for the
equation to be true. This part of the equation
ensure accuracy.
We seek to minimize the sum of coefficients
squared since that is the denominator of the margin
calculation (2 / sqrt(sum of coefficents)^2).
Therefore, minimizing the sum of coefficients
squared maximizes the margin of our equation. This
part of the equation maximizes margin.
https://quizlet.com/727988684/isye-6501-quiz-1-flash-cards/ 2/27
, 4/15/26, 6:08 PM ISYE 6501 - Quiz 1 Flashcards | Quizlet
SVM Soft Classifier: What are the "Minimize the sum of total error and square
parts and what do they represent? coefficients" We seek to minimize both in order to
minimize error while maximizing margin.
Max(0 , 1 - the sum of coefficients x variables x y) is
the portion of the equation that measures error.
Any misclassifications will produce a sum of
coefficients x variables x y value that is negative
which will be added to the total error. This part of
the equation ensure accuracy.
Like in hard classification, we seek to minimize the
sum of coefficients squared in order to maximize
margin. This part of the equation maximizes margin.
Unlike hard classification, we have the variable
lambda that is multiplied with margin which can act
as a "lever" for what we want to prioritize.
Lambda in SVM Soft Classifier Lambda is the parameter we can set to control the
trade off between margin and accuracy.
Increasing lambda increases the emphasis the
model has on margin.
Decreasing lambda increases the emphasis the
model has on accuracy.
How do you add business context to You can add the cost of a misclassification to your
build the optimal SVM? model by weighting the a0 variable (intercept).
Ex. If a bad loan (-1) is twice as costly as a good
loan (1) you can weight the a0 variable as 2/3*(a0-1)
+ 1/3(a0+1)
https://quizlet.com/727988684/isye-6501-quiz-1-flash-cards/ 3/27
