auto loans with evolver

8/3/2019 Auto Loans With Evolver

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Train-Test-

Status Age State Housin Salary Loan Amount Outcome Tag Used

new applicant 40 NJ rent 30000 15000.00 predict

new applicant 28 CT own 32000 15000.00 predict

new applicant 22 CT rent 40000 15000.00 predictnew applicant 38 CT rent 35000 15000.00 predict

new applicant 35 CT rent 31000 15000.00 predict

past applicant 30 NY rent 82000 15500.00 timely payments test

past applicant 22 CT rent 37000 22200.00 late payments test

past applicant 51 NJ own 57000 9300.00 timely payments train


past applicant 48 NY rent 58000 21400.00 timely payments train

past applicant 59 NJ rent 45000 34800.00 late payments test


past applicant 21 CT rent 17000 11300.00 default test

past applicant 20 CT own 95000 15700.00 timely payments train

past applicant 35 NY rent 92000 7100.00 timely payments trainpast applicant 29 NY own 82000 28600.00 timely payments train

past applicant 44 NY own 84000 9100.00 timely payments train


past applicant 38 NY own 35000 23200.00 timely payments test



past applicant 27 CT own 28000 17100.00 late payments test

NeuralTools Example: Auto Loans with Evolver

NeuralTools can be used to predict unknown values of a category dependent variable from known values of numeric and catvariables. In this example, a neural net learns to predict whether an auto loan applicant will be making timely payments, latethe loan.

The data set contains information on applicants who took car loans in the past, except for the first five rows, which containsA neural net has been trained, with options to Automatically Test and Automatically Predict after training, as shown in the Nesheet.

In the Detailed Report to the right of the data set, Auto-Prediction provides the predicted outcome for the new applicants, aloof those outcomes. With Live Prediction enabled, the prediction and the probability change as independent values change.lower loan amount, the predicted probability of this applicant falling within the "timely payments" classification typically will beReport to the right of the data set shows not only the probability of the predicted category, but also the probability of every poeach applicant. Those can be obtained by making appropriate selections in the Application Setting dialog: by clicking "Coluand in the table that shows selecting "Probabilities of All Categories (for PNN)" in the Prediction column.

Palisade Evolver can be used to solve the following sample optimization problem. A bank employee wants to allocate $75,0applicants so as to minimize the probability of a default occuring. That probability is calculated by the formula in cell P22. Tbeen set up as follows. The optimization goal was defined by specifying cell P22 and selecting "Minimum" as Optimizationwas specified as the adjustable range, with $0 and $30,000 as minimum and maximum values. Also, the Bugdet solving meclicking Group button, selecting Edit, and selecting Budget), to ensure the total of cells F26:F30 will be kept constant. To runEvolver Start Optimization command.

This example was created by Palisade Corporation. Note: this example requires Evolver and Industrial version of NeuralToolPrediction).


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past applicant 37 CT rent 67000 8500.00 timely payments trainpast applicant 24 NJ rent 40000 5200.00 timely payments train



past applicant 34 CT rent 78000 26900.00 timely payments train




past applicant 21 NJ rent 79000 33300.00 timely payments train



past applicant 51 NJ rent 51000 9400.00 timely payments test

past applicant 40 NJ own 67000 19300.00 timely payments trainpast applicant 60 NY own 80000 8100.00 timely payments train


past applicant 27 NJ own 34000 23600.00 late payments train

past applicant 35 NY rent 33000 28900.00 late payments train


past applicant 29 NJ rent 17000 19900.00 default train


past applicant 39 CT rent 68000 17900.00 timely payments test




past applicant 23 NY own 66000 25200.00 timely payments testpast applicant 30 NJ rent 26000 19300.00 default train



past applicant 55 CT rent 32000 34200.00 default train





past applicant 60 NJ own 67000 10200.00 timely payments test



past applicant 41 NY rent 62000 11800.00 timely payments trainpast applicant 24 NY rent 80000 31100.00 timely payments train








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past applicant 51 NJ rent 43000 30000.00 late payments trainpast applicant 21 NJ rent 74000 7500.00 timely payments train









past applicant 25 NJ rent 28000 18700.00 late payments train


past applicant 53 NJ own 92000 14600.00 timely payments trainpast applicant 44 NJ rent 29000 26700.00 default train











past applicant 56 NJ own 59000 25100.00 timely payments trainpast applicant 59 CT rent 18000 13800.00 late payments train






past applicant 37 CT own 47000 18700.00 timely payments test





past applicant 38 NJ rent 79000 14100.00 timely payments trainpast applicant 55 CT rent 38000 24200.00 late payments train








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past applicant 29 NY own 84000 29400.00 timely payments trainpast applicant 25 CT own 89000 9300.00 timely payments train








past applicant 54 CT own 22000 21800.00 late payments train



past applicant 21 CT own 46000 5600.00 timely payments trainpast applicant 58 NJ own 95000 31800.00 timely payments train

past applicant 42 NY rent 33000 26000.00 late payments test

past applicant 28 CT rent 23000 10300.00 late payments train









past applicant 43 CT own 40000 26200.00 timely payments testpast applicant 34 CT own 65000 33900.00 timely payments train











past applicant 45 NY rent 69000 26000.00 timely payments trainpast applicant 36 NJ rent 29000 13900.00 late payments train



past applicant 25 NJ own 15000 32000.00 default train





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past applicant 40 NJ own 26000 27600.00 late payments test




past applicant 24 CT own 19000 28300.00 default trainpast applicant 36 CT rent 93000 32300.00 timely payments train





past applicant 30 NY own 30000 31000.00 late payments train






past applicant 48 NJ own 60000 15300.00 timely payments testpast applicant 27 NJ rent 41000 22500.00 late payments test











past applicant 25 NJ rent 46000 23600.00 late payments trainpast applicant 25 NJ own 57000 10400.00 timely payments train

past applicant 24 NY rent 35000 24700.00 late payments test










past applicant 38 CT own 20000 32400.00 default trainpast applicant 35 NY own 55000 24600.00 timely payments train








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past applicant 22 NJ own 41000 24900.00 timely payments trainpast applicant 22 NJ own 16000 9400.00 late payments test











past applicant 58 CT own 48000 12800.00 timely payments trainpast applicant 54 NY rent 26000 17500.00 late payments train











past applicant 55 NY own 76000 8500.00 timely payments trainpast applicant 32 NJ rent 34000 29100.00 default train











past applicant 47 CT own 59000 23800.00 timely payments trainpast applicant 55 NJ rent 28000 24500.00 late payments train








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past applicant 32 NY rent 66000 18500.00 timely payments testpast applicant 21 CT own 22000 30500.00 default train











past applicant 43 NY rent 26000 23500.00 late payments trainpast applicant 26 NJ rent 17000 27900.00 default train



past applicant 47 NY rent 24000 33000.00 default train








past applicant 51 CT own 62000 26100.00 timely payments testpast applicant 27 NJ own 83000 8100.00 timely payments train











past applicant 48 NJ rent 92000 18200.00 timely payments testpast applicant 35 NY own 61000 17000.00 timely payments train








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past applicant 56 NY own 33000 18900.00 timely payments testpast applicant 40 NY own 88000 18500.00 timely payments train











past applicant 30 CT own 61000 23400.00 timely payments trainpast applicant 24 NY rent 28000 29600.00 default train









past applicant 37 CT own 17000 27200.00 default train


past applicant 45 CT own 65000 9900.00 timely payments testpast applicant 42 CT rent 44000 32800.00 late payments train











past applicant 49 NY rent 94000 17700.00 timely payments trainpast applicant 56 NY rent 76000 24400.00 timely payments test








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past applicant 36 NJ own 65000 23100.00 timely payments testpast applicant 26 CT rent 69000 9800.00 timely payments train


past applicant 58 NJ rent 41000 30300.00 late payments test









past applicant 46 NJ rent 70000 12300.00 timely payments trainpast applicant 48 NJ own 18000 8600.00 timely payments train











past applicant 29 NY own 44000 9600.00 timely payments trainpast applicant 49 NJ rent 94000 27100.00 timely payments test











past applicant 35 CT rent 62000 6500.00 timely payments trainpast applicant 20 NJ own 75000 31400.00 timely payments train





past applicant 25 NY rent 16000 29500.00 default train



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past applicant 24 CT own 23000 27700.00 late payments test


past applicant 43 CT own 57000 26400.00 timely payments trainpast applicant 20 CT own 76000 5400.00 timely payments train





past applicant 32 NY own 22000 23300.00 late payments test






past applicant 42 NY rent 78000 8900.00 timely payments trainpast applicant 28 CT rent 17000 24300.00 default test











past applicant 27 NY rent 85000 30300.00 timely payments testpast applicant 28 CT own 91000 25500.00 timely payments train











past applicant 27 NJ own 56000 28100.00 timely payments trainpast applicant 23 NJ rent 62000 31900.00 late payments train








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past applicant 33 CT rent 36000 32200.00 default testpast applicant 44 NY own 80000 31300.00 timely payments train











past applicant 35 CT rent 58000 9000.00 timely payments trainpast applicant 27 CT rent 49000 28300.00 late payments train





past applicant 22 NJ rent 38000 33900.00 default test






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Probability of default: #ADDIN?

Predict Report for Net Trained on Auto Loan Data

Prediction Prediction% Incorrect% Good/Bad timely payments% late payments% not default

#ADDIN? #ADDIN? #ADDIN? #ADDIN? #ADDIN?


#ADDIN? #ADDIN? #ADDIN? #ADDIN? #ADDIN?#ADDIN? #ADDIN? #ADDIN? #ADDIN? #ADDIN?


timely payments 100.00% 0.00% Good

late payments 96.18% 3.82% Good



default 50.93% 49.07% Good



egory independentpayments, or default on

ata on a new applicants.uralTools-Summary

ng with the probabilitiesFor example, with ahigher. The Detailed

ssible classification forns in Detailed Reports"

0 in loans to the fivee Evolver model hasoal. Range F26:F30hod was selected (bythe example, select

s (to support Live


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timely payments 70.89% 70.89% Bad



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timely payments 100.00% 0.00% Goodlate payments 97.37% 2.63% Good








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late payments 51.70% 51.70% Bad









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default 60.91% 60.94% Bad












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default%

#ADDIN?

#ADDIN?

#ADDIN?#ADDIN?

#ADDIN?


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NeuralTools: Neural Net Training, Auto-Testing, and APerformed By: Mirek Janusz

Date: Tuesday, November 24, 2009 9:33:50 AM

Data Set: Auto Loan Data

Net: Net Trained on Auto Loan Data

Summary

Net Information

Name Net Trained on Auto Loan Data

Configuration PNN Category Predictor

Location Auto Loans with Evolver.xls

Independent Category Variables 2 (State, Housing)

Independent Numeric Variables 3 (Age, Salary, Loan Amount)

Dependent Variable Category Var. (Outcome)

Training

Number of Cases 397

Training Time 0:00:02

Number of Trials 102

Reason Stopped Auto-Stopped

% Bad Predictions 0.5038%

Mean Incorrect Probability 4.7939%

Std. Deviation of Incorrect Prob. 9.8677%

Testing

Number of Cases 99

% Bad Predictions 5.0505%

Mean Incorrect Probability 8.2498%

Std. Deviation of Incorrect Prob. 17.3186%

Prediction

Number of Cases 5

Live Prediction Enabled YESData Set

Name Auto Loan Data

Number of Rows 501

Manual Case Tags YES

Classification Matrix

(for training cases)

default late payments timely payments

default 27 1 0

late payments 1 62 0

timely payments 0 0 306

Classification Matrix

(for testing cases)

default late payments timely payments

default 4 0 0

late payments 1 13 3

timely payments 0 1 77

2.00E+299

Histogram of Probability of Incorrect Categories (Training)


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0 . 0

0

0 . 1

0

0 . 2

0

0 . 3

0

0 . 4

0

0 . 5

0

-1.20E+300

-1.00E+300

-8.00E+299

-6.00E+299

-4.00E+299

-2.00E+299

F r e q u e n c y

0 . 0

0

0 . 1

0

0 . 2

0

0 . 3

0

0 . 4

0

0 . 5

0

0 . 6

0

0 . 7

0

0 . 8

0

0 . 9

0

-1.20E+300

-1.00E+300

-8.00E+299

-6.00E+299

-4.00E+299

-2.00E+299

0

2.00E+299

Histogram of Probability of Incorrect Categories (Testing)

F r e q u e n c y


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to-Predi

Bad (%)

3.5714%

1.5873%

0.0000%

Bad (%)

0.0000%

23.5294%

1.2821%


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0 . 6

0

1 . 0

0

auto loans with evolver

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