#### Transcript Extra Guidelines for How to do the Machine Learning Homework

```Homework
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What’s important (i.e., this will be used in determining your
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Finding features that make a difference
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You should expect to do some digging in the data
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Turn in a sample of your data file in ARFF format with the features
you ended up using
(5 instances only)
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An instance is a row in the data file
It contains all attributes that you will have for an individual
Turn in a Weka log documenting the series of steps you used to
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Find a feature that requires manipulation of data
Reformatting of data to provide a more consistent feature (e.g., gender, profession)
We want the experimentation that backs up your claims in the report
We will not be ranking your models (too hard for you to turn in
enough so we can do that)
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Lattitude
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to think and make decisions with the data. It
is not cut and dried for you.
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There are some 200 attributes
You will not want to experiment with all of them
Make some choices about which ones you think
are important. These are your “hypotheses”
Then test whether your hypotheses were correct
Important: Remember the KDD presentation.
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Size can often win out over importance of a feature
The authors normalized by length
 E.g., for word frequency in the paper body vector,
the abstract vector and the title vector, they
normalized (divided) by length. Why?
You should think about normalization also.
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Steps you should follow
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Divide your data into training and testing
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Do attribute selection first
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Compare results using a chart
With the sets of attributes and machine learning program that
you selected, vary the data
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Decide on the attributes you want to experiment with
Systematically measure their impact on accuracy (as in the greedy-stepwise
algorithm and the KDD paper)
Use cross-validation on the training set to do this OR divide the training set
further into training and augmentation validation
Make charts (using Excel or other chart making program). Weka’s charts are
not clear.
With the set of attributes that you determine are good, now run
twice, once with Bayesian Nets and once with Decision Trees.
Note linear regression only appropriate when used with numbers,
so only appropriate for donation amount.
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Use “Florida” for testing. Everything else for training
Don’t use Florida at all until you’re finished.
Run with increments of 10%, showing the accuracy. Describe what you see.
Choose the resulting model and run it on the test data (Florida)
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Provide your accuracy results on the test
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