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Neural Network Tool

The Neural Network tool creates a feedforward perceptron neural network model with a single hidden layer. The neurons in the hidden layer use a logistic (also known as a sigmoid) activation function, and the output activation function depends on the nature of the target field. Specifically, for binary classification problems (e.g., the probability a customer buys or does not buy), the output activation function used is logistic, for multinomial classification problems (e.g., the probability a customer chooses option A, B, or C) the output activation function used is softmax, for regression problems (where the target is a continuous, numeric field) a linear activation function is used for the output.

Neural networks represent the first machine learning algorithm (as opposed to traditional statistical approaches) for predictive modeling. The motivation behind the method is mimicking the structure of neurons in the brain (hence the method's name). The basic structure of a neural network involves a set of inputs (predictor fields) that feed into one or more "hidden" layers, with each hidden layer having one or more "nodes" (also known as "neurons").

In the first hidden layer, the inputs are linearly combined (with a weight assigned to each input in each node), and an "activation function" is applied to the weighted linear combination of the predictors. In the second and subsequent hidden layers, output from the nodes of the prior hidden layer are linearly combined in each node of the hidden layer (again with weights assigned to each node from the prior hidden layer), and an activation function is applied to the weighted linear combination. Finally, the results from the nodes of the final hidden layer are combined in a final output layer that uses an activation function that is consistent with the target type.

Estimation (or "learning" in the vocabulary of the neural network literature) involves finding the set of weights for each input or prior layer node values that minimize the model's objective function. In the case of a continuous numeric field this means minimizing the sum of the squared errors of the final model's prediction compared to the actual values, while classification networks attempt to minimize an entropy measure for both binary and multinomial classification problems. As indicated above, the Neural Network tool (which relies on the R nnet package), only allows for a single hidden layer (which can have an arbitrary number of nodes), and always uses a logistic transfer function in the hidden layer nodes. Despite these limitations, our research indicates that the nnet package is the most robust neural network package available in R at this time.

While more modern statistical learning methods (such as models produced by the Boosted, Forest, and Spline Model tools) typically provide greater predictive efficacy relative to neural network models, in some specific applications (which cannot be determined before the fact), neural network models outperform other methods for both classification and regression models. Moreover, in some areas, such as in financial risk assessment, neural network models are considered a "standard" method that is widely accepted.

This tool uses the R programming language. Go to Options > Download Predictive Tools to install R and the packages used by the R Tool.

Input

An Alteryx data stream that includes a target field of interest along with one or more possible predictor fields.

Configuration Properties

Required Parameters

Model Customization

Graphics Options

Output

https://en.wikipedia.org/wiki/Artificial_neural_network