# Demo

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This demo will introduce you to the "Sensitivity About the Mean" testing process.  By performing sensitivity analysis on a trained network you can often find and eliminate irrelevant inputs.  The elimination of irrelevant inputs reduces data collection cost and can sometimes improve a network's performance.  Furthermore, sensitivity analysis can give insights into the underlying relationships between the inputs and outputs.


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In this demo we will develop a model for real estate appraisal in the Boston area.  We will use 13 indicators as inputs to this model.  These indicators are per capita crime rate by town (CRIM), proportion of residential land zoned for lots over 25,000 sq.ft. (ZN), proportion of non-retail business acres per town (INDUS), bounds Charles River (CHAS), nitric oxides concentration (NOX), average number of rooms per dwelling (RM), proportion of owner-occupied units built prior to 1940 (AGE), weighted distances to five Boston employment centers (DIS), index of accessibility to radial highways (RAD), full-value property-tax rate per $10,000 (TAX), pupil-teacher ratio by town (PTRATIO), 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town (B), % lower status of the population (LSTAT).


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The desired output for this model is the Median value of owner-occupied homes (in $1000's).  There are 400 total samples.  Three hundred of them have been tagged as "Training" and the other 100 have been tagged as "Testing".  The data is located in the sheet named "Housing Data" located behind this slide.


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A generalized feed forward (GFF) network has been pre-trained on this data and the results are shown in the worksheet named "TrainGFF Report" located behind this slide.  Furthermore, the trained network has also been pre-tested on the data tagged as "Testing".  Click the "Test Report" button now to view the testing results.  Examine the various performance measures presented in the report.  Also, click on the button labeled "TestGFF IOData" in the upper right-hand corner of the report worksheet.  This will take you to a sheet containing the inputs used for testing, the desired output (MEDV Desired), and the actual network output (MEDV Output).  Examine the "MEDV Desired" and "MEDV Output" columns and notice that in most cases, the output of the neural network model comes very close to the desired value.


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In this step we will run the "Sensitivity About the Mean"  testing process on the pre-trained GFF network.  This batch starts by varying the first input between its mean +/- a user defined number of standard deviations while all other inputs are fixed at their respective means. The network output is computed for a user defined number of steps above and below the mean.  This process is then repeated for each input.  Finally, a report is generated which summarizes the variation of each output with respect to the variation of each input.  Click  the "Run Sensitivity Analysis" button now to run this batch process.  Examine the settings in the resulting dialog box then click OK.


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Examine the resulting sensitivity analysis report contained within the sheet named "Sensitivity Report" located behind this slide.  Notice that according to the sensitivity analysis, the six most important inputs are whether the property bounds the Charles River (CHAS), nitric oxides concentration (NOX), average number of rooms per dwelling (RM), weighted distances to five Boston employment centers (DIS), pupil-teacher ratio by town (PTRATIO), and % lower status of the population (LSTAT).


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In this step we will train and test the network using only the 6 most significant inputs found in the last step.  We will use the data sheet named "Housing Data 6" located behind this slide in which only CHAS, NOX, RM, DIS, PTRATIO, and LSTAT have been tagged as "Input".  Click  the "Run Train Test" button now to train and test this new configuration.  Examine the resulting training report ("TrainGFF6 Report") and testing report ("TestGFF6 Report") and convince yourself that this new model performs just as well as the old model even after disregarding 7 of the 13 inputs used in the previous model.


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This demo has illustrated how easy it is to use the "Sensitivity About the Mean" testing process to analyze your trained network.  This batch process provides insight into what the network has actually learned in addition to exposing irrelevant inputs.  Click "Next" to return to the main demo panel.


