Further Results Analysis

It is important to understand the process of building the model in order to further analyze the results.

Running the model will first generate the derivable columns from all the predictor variables selected and building bands where appropriate. The tool then works out which of these are related and ignore columns that are extraneous. For example if both the 'Age' and 'Date of Birth' columns are selected one of these is ignored by the model.

The two 2,500 row groups of data is then built. These records are then used to build the model using a technique that gives coverage of all the selected variables to ensure that significant attributes that are only lightly represented are given the precedence that they deserve. Traditional random sampling methods may sample out too many of these significant attributes to make them statistically useful.

A progress window is displayed as the job processes, and once complete the results are available.

• Predict Inclusion Tool
  • Creating a Predict Inclusion Report
  • Analyzing the Results
  • Further Results Analysis