The Boston sample data table contains data on 13 factors that might relate to median home values. You fit a model using a neural network. Because neural networks do not accommodate formal hypothesis tests, these tests are not available to help assess which variables are important in predicting the response. However, for this purpose, you can use the Assess Variable Importance profiler option.
Note that your results might differ from, but should resemble, those shown here. There are two sources of random variability in this example. When you fit the neural network, k-fold cross validation is used. This partitions the data into training and validation sets at random. Also, Monte Carlo sampling is used to calculate the factor importance indices.
Select Help > Sample Data Library and open Boston
Select Analyze > Predictive Modeling > Neural.
Select mvalue from the Select Columns list and click Y, Response.
In the Neural Model Launch panel, select KFold from the list under Validation Method.
From the red triangle menu next to Prediction Profiler, select Assess Variable Importance > Dependent Resampled Inputs.
The Variable Importance: Dependent Resampled Inputs report appears (Dependent Resampled Inputs Report). Check that the Prediction Profiler cells have been reordered by the magnitude of the Total Effect indices in the report. In Dependent Resampled Inputs Report, check that the Total Effect importance indices identify rooms and lstat as the factors that have most impact on the predicted response.
Dependent Resampled Inputs Report
From the red triangle menu next to Prediction Profiler, select Assess Variable Importance > Independent Resampled Inputs.
The resampled inputs option makes sense in this example, because the distributions involved are not uniform. The Variable Importance: Independent Resampled Inputs report is shown in Independent Resampled Inputs Report. Check that the two factors identified as having the most impact on the predicted values are lstat and rooms. Note that the ordering of their importance indices is reversed from the ordering using Dependent Resampled Inputs.
Independent Resampled Inputs Report

Help created on 9/19/2017