Module 1
Ayics important types of questions r
Descriptive-what happened?
-
-
Predictive what
-
will happen? Uncertantity wh outcomes
-
Prescriptive -
What action(s) would be best?
-
More general questions
Modding
-
Means taking a real life situation and expressing the key parts of that situation in terms of math.
O Describe a real life situation mathematically
② Analyze the math
③ Turn mathematical answer back into real-life situation
se
strut
at
Cuttingstep
Reg Output Quality
-
ression
-
-
Optimization -
Missing Data
-
Etc .
-
Etc
What is Modeling?
-
Real life situation
pressed as math
model
This study source was downloaded by 100000900934077 from CourseHero.com on 10-08-2025 13:25:53 GMT -05:00
https://www.coursehero.com/file/251983907/Mod-1-Intropdf/
,Module
2Classification
: is
Putting things into categories
-
-
As simple as yes or no
Able to Differentiate
-
More than just 2 categories
-
Classification Questions
ex-Bankloan -
Income age assets credit
, , ,
Blue dots are those who paid back a loan
Red are those who did not (defaulted
draw this line to determine if want to loan
-
we
give a
But you can draw
-
an infinite number of lines between these so what do you do ?
,
classifier
higher
L Here is a version with 2 lines , called separators
lowerossifiers or classifiers.
Blue above and red below.
-
-
what about this applicant in yellow?
The lower classifer says approve higher
deny
-
,
What do we do?
, In want to chose from
general line that's further making mistakes
-
, a
-
Suppose we made this line to separate ,
its
pretty close to
a red point and blue point which
,
means that it almost
-
makes couple of mistakes in classification.
a
If the line or the points were a little different ,
the line
might misclassify them .
Now suppose this line , much further from
any points not
-
we use ,
so close to making mistakes Would take much
.
bigger error
in the data to cause misclassification .
That is more what for .
looking
-
we are
This data now has no way to perfectly separate between the blue and red points.
We need soft
classifier. that gives
One us
good
-
a
as separation possible
as ,
hard classifier
ratherthan a
that separates perfectly
-
This line minimizes the number of incorrectly classified
points. There are three (in yellow) incorrect points.
But, it close to of mistakes. (circled)
making a lot
-
is
, If we filt and slide that line like this we get a ,
few more mistakes fewertotal mistakes and near
,
Mistakes .
Trading off mistakes and near mistakes depending , on now
important we think each one is.
How important
something is?
cost of each mistake is not always equal.
-
In loan
example-cost of giving a loan that is not repaid is more than
denying a loan mistakenly.
-
.
-
How change def of the best separator?
-
Ex The more costly one type of bad decision is
.
,
the more we want to more the line away from it.
In this ex ,
we determine the cost of a defaulted
loan Cred dots) double the cost of
is
denying a
good loan ,
so we ift the line closer to the
blue points than the red points .
New applicant in yellow-status ambiguous,
-
is
it's between the blue+ red points , our new
cost-concious classifier will
suggest deny.
Ayics important types of questions r
Descriptive-what happened?
-
-
Predictive what
-
will happen? Uncertantity wh outcomes
-
Prescriptive -
What action(s) would be best?
-
More general questions
Modding
-
Means taking a real life situation and expressing the key parts of that situation in terms of math.
O Describe a real life situation mathematically
② Analyze the math
③ Turn mathematical answer back into real-life situation
se
strut
at
Cuttingstep
Reg Output Quality
-
ression
-
-
Optimization -
Missing Data
-
Etc .
-
Etc
What is Modeling?
-
Real life situation
pressed as math
model
This study source was downloaded by 100000900934077 from CourseHero.com on 10-08-2025 13:25:53 GMT -05:00
https://www.coursehero.com/file/251983907/Mod-1-Intropdf/
,Module
2Classification
: is
Putting things into categories
-
-
As simple as yes or no
Able to Differentiate
-
More than just 2 categories
-
Classification Questions
ex-Bankloan -
Income age assets credit
, , ,
Blue dots are those who paid back a loan
Red are those who did not (defaulted
draw this line to determine if want to loan
-
we
give a
But you can draw
-
an infinite number of lines between these so what do you do ?
,
classifier
higher
L Here is a version with 2 lines , called separators
lowerossifiers or classifiers.
Blue above and red below.
-
-
what about this applicant in yellow?
The lower classifer says approve higher
deny
-
,
What do we do?
, In want to chose from
general line that's further making mistakes
-
, a
-
Suppose we made this line to separate ,
its
pretty close to
a red point and blue point which
,
means that it almost
-
makes couple of mistakes in classification.
a
If the line or the points were a little different ,
the line
might misclassify them .
Now suppose this line , much further from
any points not
-
we use ,
so close to making mistakes Would take much
.
bigger error
in the data to cause misclassification .
That is more what for .
looking
-
we are
This data now has no way to perfectly separate between the blue and red points.
We need soft
classifier. that gives
One us
good
-
a
as separation possible
as ,
hard classifier
ratherthan a
that separates perfectly
-
This line minimizes the number of incorrectly classified
points. There are three (in yellow) incorrect points.
But, it close to of mistakes. (circled)
making a lot
-
is
, If we filt and slide that line like this we get a ,
few more mistakes fewertotal mistakes and near
,
Mistakes .
Trading off mistakes and near mistakes depending , on now
important we think each one is.
How important
something is?
cost of each mistake is not always equal.
-
In loan
example-cost of giving a loan that is not repaid is more than
denying a loan mistakenly.
-
.
-
How change def of the best separator?
-
Ex The more costly one type of bad decision is
.
,
the more we want to more the line away from it.
In this ex ,
we determine the cost of a defaulted
loan Cred dots) double the cost of
is
denying a
good loan ,
so we ift the line closer to the
blue points than the red points .
New applicant in yellow-status ambiguous,
-
is
it's between the blue+ red points , our new
cost-concious classifier will
suggest deny.
