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Neural Networks Automatic Model Building (Machine Learning) Artificial Intelligence 7-2 High-Growth Product • Used for classifying data – target customers – bank loan approval – hiring – stock purchase – trading electricity – DATA MINING • Used for prediction McGraw-Hill/Irwin ©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-3 Description • Use network of connected nodes (in layers) • Network connects input, output (categorical) – inputs like independent variable values in regression – outputs: {buy, don’t} {paid, didn’t} {red, green, blue, purple} {character recognition - alphabetic characters} McGraw-Hill/Irwin ©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-4 Network Input Layer Hidden Layers Output Layer Good Bad McGraw-Hill/Irwin ©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-5 Operation • Randomly generate weights on model – based on brain neurons • input electrical charge transformed by neuron • passed on to another neuron – weight input values, pass on to next layer – predict which of the categorical output is true • Measure fit – fine tune around best fit McGraw-Hill/Irwin ©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-6 Operation • Useful for PATTERN RECOGNITION • Can sometimes substitute for REGRESSION – works better than regression if relationships nonlinear – MAJOR RELATIVE ADVANTAGE OF NEURAL NETWORKS: YOU DON’T HAVE TO UNDERSTAND THE MODEL McGraw-Hill/Irwin ©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-7 Neural Network Testing • Usually train on part of available data – package tries weights until it successfully categorizes a selected proportion of the training data • When trained, test model on part of data – if given proportion successfully categorized, quits – if not, works some more to get better fit • The “model” is internal to the package • Model can be applied to new data McGraw-Hill/Irwin ©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-8 Business Application • Best in classifying data mortgage underwriting bond rating commodity trading asset allocation fraud prevention • Predicting interest rate, inventory firm failure bank failure takeover vulnerability stock price corporate merger profitability McGraw-Hill/Irwin ©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-9 Neural Network Process 1. Collect data 2. Separate into training, test sets 3. Transform data to appropriate units • Categorical works better, but not necessary 4. Select, train, & test the network • • • Can set number of hidden layers Can set number of nodes per layer A number of algorithmic options 5. Apply (need to use system on which built) McGraw-Hill/Irwin ©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-10 Marketing Applications • Direct marketing – database of prospective customers • age, sex, income, occupation, education, location • predict positive response to mail solicitations • THIS IS HOW DATA MINING CAN BE USED IN MICROMARKETING McGraw-Hill/Irwin ©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-11 Neural Nets to Predict Bankruptcy Wilson & Sharda (1994) Monitor firm financial performance Useful to identify internal problems, investment evaluation, auditing Predict bankruptcy - multivariate discriminant analysis of financial ratios (develop formula of weights over independent variables) Neural network - inputs were 5 financial ratios - data from Moody’s Industrial Manuals (129 firms, 1975-1982; 65 went bankrupt) Tested against discriminant analysis Neural network significantly better McGraw-Hill/Irwin ©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-12 Ranking Neural Network Wilson (1994) Decision problem - ranking candidates for position, computer systems, etc. INPUT - manager’s ranking of alternatives Real decision - hire 2 sales people from 15 applicants Each applicant scored by manager Neural network took scores, rank ordered best fit to manager of alternatives compared (AHP) McGraw-Hill/Irwin ©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-13 CASE: Support CRM Drew et al. (2001), Journal of Service Research • Identify customers to target • Customer hazard function: – Likelihood of leaving to a competitor (CHURN) • Gain in Lifetime Value (GLTV) – NPV: weight EV by prob{staying} – GLTV: quantified potential financial effects of company actions to retain customers McGraw-Hill/Irwin ©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-14 Models: • Proportional Hazards Regression • Neural Networks – Estimate hazard functions • Baseline Regression Models – Models for longitudinal analysis McGraw-Hill/Irwin ©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-15 Data: Data Warehouse of Cellular Telephone Division • Billing – Previous balance, access charges, minutes used, toll charges, roaming charges, optional features • Usage – Number of calls, minutes by local, toll, peak, off-peak • Subscription – Months in service, rate plan, contract type, date, duration • Churn – Binary flag • Demographics – Age, profitability to firm (current & future) McGraw-Hill/Irwin ©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-16 Model Use • Sample of 21,500 subscribers, April 1998 • Modeled tenure for 1 to 36 months • Trained on 15,000 of these samples – Remainder used for testing • Neural network models worked better than traditional statistics McGraw-Hill/Irwin ©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-17 Systems A great many products • general NN products $59 to $2,000 @Brain BrainMaker Discover-It • components DATA MINING along with megadatabases other products • library callable • specialty products construction bidding, stock trading, electricity trading McGraw-Hill/Irwin ©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-18 Potential Value • THEY BUILD THEMSELVES – humans pick the data, variables, set test limits • CAN DEAL WITH FAST-MOVING SITUATIONS – stock market • CAN DEAL WITH MASSIVE DATA – data mining • Problem - speed unpredictable McGraw-Hill/Irwin ©2007 The McGraw-Hill Companies, Inc. All rights reserved