The output of the hidden nodes is a weighted sum of the input values. Nodes in the hidden layer receive input from the input layer. Once the layer is added, enter the desired Neurons. For more information on this new feature, see the Rescale Continuous Data section within the Transform Continuous Data topic that occurs earlier in this Help.Ĭlick Add Layer to add a hidden layer. Analytic Solver Data Mining provides the following methods for feature scaling: Standardization, Normalization, Adjusted Normalization and Unit Norm.
For more information on partitioning, please see the Data Mining Partitioning chapter.Ĭlick Rescale Data to open the Rescaling dialog. U se Rescaling to normalize one or more features in your data during the data preprocessing stage. If partitioning has already occurred on the dataset, this option will be disabled. Analytic Solver Data Mining will partition your dataset (according to the partition options you set) immediately before running the regression method. Note: The Neural Network Automatic Regression - Parameters dialog does not include Architecture, but is otherwise the same.Īnalytic Solver Data Mining includes the ability to partition a dataset from within a classification or regression method by clicking Partition Data Parameters dialog. See below for options appearing on the Neural Network Regression - Parameters dialog. Neural Networks Prediction dialog, Parameters tab
Select the variable whose outcome is to be predicted here. The Neural Network Regression algorithm will accept non-numeric categorical variables. Place categorical variables from the Variables listbox to be included in the model by clicking the > command button. Variables listed here will be utilized in the Analytic Solver Data Mining output. The following options are included on both Neural Network Regression dialogs, Manual and Automatic.Īll variables in the dataset are listed here. Neural Networks Prediction dialog, Data tab The options below appear on the Neural Network Prediction - Data dialog.
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