SANN - Results - Predictions Tab
You can select the Predictions tab of the SANN - Results dialog box to access the options described here. For information on the options that are common to all tabs, see SANN - Results.
Option | Description |
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Predictions spreadsheet | |
Predictions type | You can select the set of predictions to be included in the prediction spreadsheet. Note that ensemble options are only available if more than one network is displayed in the Active neural network grid.
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Predictions | Generates a spreadsheet of model predictions. The precise details shown are controlled by the options selected in the Predictions type, Include and Samples group boxes. When creating spreadsheets, SANN always tries to present the results in as much of a compact form as possible. For example, when you create a predictions spreadsheet for a number of active networks, SANN normally includes the predictions in one single spreadsheet. However, this is only possible when the networks use the same train/test/validation samples, which is always the case for Automatic Network Search (ANS) and Custom Neural Networks (CNN) network building strategies. However, since Subsampling uses different permutations of the data set to create train/test/validation samples for each and every individual network, what constitutes a training case for one network may be a test or validation case (or none of the above) for another. In this case, it is no longer possible to consistently present the network outputs in one single spreadsheet. Therefore, whenever your active networks are created using different samples, SANN creates a predictions spreadsheet for each individual network plus one more for the ensemble (should you request ensemble outputs). Otherwise, all network and ensemble predictions are placed in one spreadsheet. |
Include | Use the options in this group box to (optionally) include additional information in the predictions spreadsheet. Available options are dependent on the analysis type and network type.
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Note: Statistica Neural Networks (SANN) solves the problem of classification by assigning true probabilities to class memberships to instances of the input (independent) variables. An input vector (data case) can assume membership of one of the classes found in the target (dependent) variable. For example, in a binary classification task (a classification problem with a target variable having two categorical levels), if the class probabilities for a given input are 0.6 and 0.4, pertaining to classes A and B, respectively, then the input is assigned to category A (since A has the highest probability). However, it may happen sometimes that the class probability memberships are equal. In this case no classification is possible. This problem, however, occurs mainly in binary-classification (as opposed to multi-classification) tasks and only when the network is poorly trained or if the data set has no clear cut or well defined boundaries between its clusters. When such circumstances occur, the predictions spreadsheet displays
unknown in the appropriate cell of the predictions spreadsheet and is highlighted in red.
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