Select the Details* *tab of the *SANN - Results* dialog box to access the
options described here. For information about the options that are common
to all tabs, see *SANN
- Results*.

**Summary.** Click the *Summary*
button to generate a spreadsheet containing the summary details listed
in *the Active neural networks*
grid box.

**Weights.** Click the *Weights*
button to display a spreadsheet of weights and threshold for each model
in the *Active neural networks*
grid.

**Correlation coefficients.** Click the
Correlation coefficients button
to generate a spreadsheet of correlations coefficients for the target
variables (for train, test, and validation samples) using the active neural
networks models. Note that this option is only available for regression
and time series (regression) analyses.

**Confusion
matrix.** Click the *Confusion
matrix* button to generate a confusion matrix and classification summary
for the categorical target. A confusion matrix gives a detailed breakdown
of misclassifications. The observed class is displayed at the top of the
matrix, and the predicted class down the side; each cell contains a number
showing how many cases that were actually of the given observed class
were assigned by the model to the given predicted class. In a perfectly
performing model, all the cases are counted in the leading diagonal. A
classification summary gives the total number of observations in each
class of the target, the number of correct and incorrect predictions for
each class, and the percentage of correct and incorrect predictions for
each class. This information is provided for each active network. Only
the samples selected in the *Sample*
group box (e.g., *Train*) will
be used in generating the two spreadsheets. Note this option is only available
for classification and time series (classification).

**Confidence levels.** Click the *Confidence
levels* button to display a spreadsheet of confidence levels for each
case in the sample selected in the *Sample*
group box. Confidence levels will be displayed for each model. You can
specify which cases to include in the spreadsheet using the options in
the *Sample* group box. Note
that this option is only available for classification problems.

**Predictions statistics.** Click the *Predictions statistics* button
to generate a spreadsheet containing minimum and maximum prediction values,
residuals, and standardized residuals for each model in the *Active
neural networks* grid. These statistics will be reported for each sample
(training, testing and validation). This option is only available for
regression and time series (regression) analyses.

**Data statistics.** Click the *Data
statistics* button to generate a spreadsheet containing the mean, standard
deviation, minimum value and maximum value for each continuous variable
in the analysis. These data statistics will be broken down by each sample
(training, testing, and validation) and also reported for the overall
data set.

**Global
sensitivity analysis.** Click the *Global
sensitivity analysis* button to conduct a sensitivity analysis on each
model and display the results in a spreadsheet. Sensitivity analysis rates
the importance of the models' input variables. You can conduct sensitivity
analysis on a per sample basis, using the options in the *Sample*
group box.

Note:

Global sensitivity for continuous input: Error when input is set to mean divided by error when input is used.

Global sensitivity for categorical input: Average error when input is set to all other categorical levels divided by error when input is used.

For regression, error is sum of squares. For classification, error is cross-entropy. If variable is important, global sensitivity should be large (>>1).

**Local
sensitivity analysis.** Click *Local
sensitivity analysis* button to generate a separate spreadsheet of
local (pointwise) sensitivity analysis for each model in the *Active
neural networks* grid. Local sensitivity analysis indicates how sensitive
the output of a neural network is to a given domain of an input variable.
These sensitivity values are actual first-order derivatives evaluated
at specific centile points for each input. For each input the derivative
is taken with respect to the target at ten evenly spaced locations with
the observed minimum and maximum values serving as end points. Other input
variables are set to their means during this calculation. A separate spreadsheet
is also generated for each dependent (target) variable as well. You can
conduct pointwise sensitivity analysis on a per sample basis, using the
options in the *Sample* group
box. Note that this option is only available for regression analyses with
no categorical inputs.