Database Reference
In-Depth Information
8)
Drag your two data sets into a new process window. If you have prepared your data well
in OpenOffice Calc, you shouldn't have any missing or inconsistent data to contend with,
so data preparation should be minimal. Rename the two retrieve operators so you can tell
the difference between your training and scoring data sets.
9)
One necessary data preparation step is to add a Set Role operator and define the Graduated
attribute as your label in your training data. Alternatively, you can set your Graduated
attribute as the label during data import.
10)
Add a Logistic Regression operator to your Training stream.
11)
Apply your Logistic Regression model to your scoring data and run your model. Evaluate
and report your results. Are your confidence percentages interesting? Surprising? Do the
predicted Graduation values seem reasonable and consistent with your training data? Does
any one independent variable (predictor attribute) seem to be a particularly good predictor
of the dependent variable (label or prediction attribute)? If so, why do you think so?
Challenge Step!
12)
Change your Logistic Regression operator to a different type of Logistic operator
(for example, maybe try the Weka W-Logistic operator). Re-run your model. Consider
doing some research to learn about the difference between algorithms underlying different
logistic approaches. Compare your new results to the original Logistic Regression results
and report any interesting findings or differences.
