Task Generate test design
Prior to building a model, a procedure needs to be defined to test the model’s quality and validity. For example, in supervised data mining tasks such as classification, it is
common to use error rates as quality measures for data mining models. Therefore the test design specifies that the dataset should be separated into training and test set, the model is built on the training set and its quality estimated on the test set.
Output Test design
Describe the intended plan for training, testing and evaluating the models. A primary component of the plan is to decide how to divide the available dataset into training data, test data and validation test sets.
Activities Check existing test designs for each data mining goal separately. Decide on necessary steps (number of iterations, number of folds etc.). Prepare data required for test. (You can use 66% of records for model Building and rest for Testing)
2.3 Build model
Task Build model
Run the modeling tool on the prepared dataset to create one or more models. (Using Weka or Knime or any Tool of your choice)
Output Parameter settings
With any modeling tool, there are often a large number of parameters that can be adjusted. List the parameters and their chosen values, along with the rationale for the choice.
Activities Set initial parameters. Document reasons for choosing those values.
Output Models Run the modeling tool on the prepared dataset to create one or more models.
Activities Run the selected technique on the input dataset to produce the model. Post-process data mining results (e.g. editing rules, display trees).
Output Model description
Describe the resultant model and assess its expected accuracy, robustness and possible shortcomings. Report on the interpretation of the models and any difficulties encountered.
You can add the screenshots of the various output you go when you run the Model.

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