1) Import your two CSV files into your RapidMiner repository. Be sure to give them descriptive names. (or you can use the datasets you previously prepared for the Linear Regression Exercise)
2) Drag your two data sets into a new process window. If you have prepared your data well, 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.
3) Select Attributes to predict your target attribute, including the target attribute.
4) One necessary data preparation step is to add a Set Role operator and define the target attribute( i.e., Outgoing in adulthood) as your label in your training data. Alternatively, you can set your target attribute as the label during data import.
8) Add a Cross Validation operator. In the first part of the sub-process, add a Logistic Regression operator as we did previously to build a model, and in the second part, we will apply our model (Apply Model) and check its performance (Classification).
9) Apply your Logistic Regression model to your scoring data and run your model. Evaluate and report (450-500 words) findings. Are your confidence percentages interesting? Surprising? Do the predicted target values seem reasonable and consistent with your training data? Does any 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? Change your Logistic Regression operator to a different type of Logistic operator (for example, maybe try the Weka W-Logistic operator. You may need to install "Weka" extension by visiting Extensions - Marketplace). 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.
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