Weight presentation
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Efficient Frontier Analytic TeamJuly , 2010
Using LARS Regression for
Objective Function
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Goal
Find the best set of weights across the whole loan channel
that best capture the loan intent and conversion
behaviors to help optimize SEM optimization.
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Correlation Analysis
Auto TD and UW_TD have positive correlation with Total.Apps
Total.Loans has strong correlation with Total Amount, so we will only put
Total.Loans in the objective function.
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Variable Selection: LARS Algorithm
Model Total.Apps AutoTD UW_TD ApprovedPredictive
Error
0 0 0 0 0 1.36
1 0 0 0 0.130 0.81
2 0.007 0 0 0.159 0.73
3 0.010 0 -0.003 0.156 0.75
4 0.027 0.024 -0.028 0.136 0.78
Model 2 captures the best relationship between Total Loan and
other variable best.
By using this model, we can give best prediction of Total Loan
with lowest predictive error based on other variables.
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Recommended Objective Function
LoanTotalApprovedAppsTotalObjective .116.0.007.0
Total.Apps captures the volume of loan applications
Approved captures the volume of qualified applications
Total Loan captures the final confirmed loan.