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Predictive Modeling with SAS Enterprise Miner, 2nd Edition by Kattamuri S. Sarma, PhD

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Index

A

accuracy criterion 193–194

acquisition cost 417–418

activation functions

about 243, 322

output layer 247

target layer 270–272

Add value 306

adjusted frequencies 441

adjusted probabilities, expected profits using 236

AIC (Akaike Information Criterion) 350–352

Append node 48–50, 116

Arc Tanget function 243–244

Architecture property

about 316

MLP setting 247

Neural Network node 281, 283, 284, 293, 295–297, 298–300, 305

NRBFUN network 303–304

Regression node 389

architectures

alternative built-in 286–307

of neural networks 316

user-specified 305–307

Assessment Measure property 174, 187, 193, 198, 387–389, 396–399

attrition, predicting 384–392

auto insurance industry, predicting risk in 3–4

AutoNeural node 307–309, 314–315, 316

Average method ...

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