Chap9-Neural%2bNets

# Chap9-Neural%2bNets - Chapter 9 Neural Nets Data Mining for...

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Chapter 9 – Neural Nets © Galit Shmueli and Peter Bruce 2008 Data Mining for Business Intelligence Shmueli, Patel & Bruce

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Basic Idea Combine input information in a complex & flexible neural net “model” Model “coefficients” are continually tweaked in an iterative process The network’s interim performance in classification and prediction informs successive tweaks
Network Structure Multiple layers Input layer (raw observations) Hidden layers Output layer Nodes Weights (like coefficients, subject to iterative adjustment) Bias values (also like coefficients, but not subject to iterative adjustment)

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Schematic Diagram
Example – Using fat & salt content to predict consumer acceptance of cheese

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Example - Data
Moving Through the Network

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The Input Layer For input layer, input = output E.g., for record #1: Fat input = output = 0.2 Salt input = output = 0.9 Output of input layer = input into hidden layer
The Hidden Layer In this example, it has 3 nodes Each node receives as input the output of all input nodes Output of each hidden node is a function of the weighted sum of inputs

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The Weights The weights θ (theta) and w are typically initialized to random values in the range -0.05 to +0.05 Equivalent to a model with random prediction (in other words, no predictive value) These initial weights are used in the first round of training
Output of Node 3 if g is a Logistic Function

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Initial Pass of the Network
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Chap9-Neural%2bNets - Chapter 9 Neural Nets Data Mining for...

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