Download Learning JMP_.ppt

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project

Document related concepts

Multinomial logistic regression wikipedia , lookup

Transcript
Using JMP for the Case
Competition
1-2
Overview of Case Analysis
If you have not had formal coursework in data
mining, in order to compete in the case, you will
probably want to do the following:
• Install JMP
• Learn the basics of JMP
• Learn about partitioning the data set
(training, validation, test sets)
• Learn about specifying the type of variables
(nominal, ordinal, categorical)
1-3
Overview of Case Analysis
Learn about specific modeling techniques like:
• Logistic Regression
• Decision Trees
• Bootstrap Forest
• Boosted Trees
• Neural Net Models
1-4
Installing JMP
Villanova owns a site license for JMP so that
every student can install JMP at:
https://software.villanova.edu/
Enter you Villanova user id and password (keep
the organization box blank). Windows users will
select JMP and Mac users will select JMP (OSX).
The functionality is the same in both versions but
there are some differences in navigation and
menuing.
1-5
JMP Tutorials
Tutorials videos for students using JMP can
be found at
http://www.jmp.com/en_us/learninglibrary.html
1-6
On-Demand Webcasts
There are several good on-demand webcasts that
provide a good overview of data mining, provide a
discussion of data partitioning, explain where to
access the sample data sets within JMP, and
provide an introduction to building predictive
models.
http://www.jmp.com/about/events/ondemand/
1-7
On-Demand Webcasts
Additional webcasts are in Building Better Models:
http://www.jmp.com/en_us/events/ondemand/b
uilding-better-models.html
1-8
Model Comparison
A good model comparison video can be found at:
http://www.jmp.com/en_us/events/ondemand/b
uilding-better-models/introduction-modelingand-model-comparison.html
1-9
On-Demand Webcasts
Consider watching JMP’s :
•
•
•
•
Regression video
Decision tree video
Bootstrap Forest and Boosted Tree videos
Neural Net videos
We hope this help and Good Luck!