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ML model used in Nussknacker ML scenario in the demo.nussknacker.io site
<?xml version="1.0" encoding="UTF-8"?>
<!--https://access.redhat.com/documentation/en-us/red_hat_process_automation_manager/7.9/html/developing_decision_services_in_red_hat_process_automation_manager/ai-credit-card-con_artificial-intelligence-->
<PMML xmlns="http://www.dmg.org/PMML-4_2" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" version="4.2" xsi:schemaLocation="http://www.dmg.org/PMML-4_2 http://www.dmg.org/v4-2-1/pmml-4-2.xsd">
<Header copyright="Copyright (c) 2018 Software AG" description="Default Description">
<Application name="Nyoka" version="4.3.0" />
<Timestamp>2020-10-09 14:27:26.622723</Timestamp>
</Header>
<DataDictionary numberOfFields="3">
<DataField name="debt_amount" optype="continuous" dataType="double" />
<DataField name="account_balance" optype="continuous" dataType="double" />
<DataField name="dispute_risk" optype="categorical" dataType="integer">
<Value value="1" />
<Value value="2" />
<Value value="3" />
<Value value="4" />
<Value value="5" />
</DataField>
</DataDictionary>
<TreeModel modelName="CreditRiskClassifier" functionName="classification" missingValuePenalty="1.0">
<MiningSchema>
<MiningField name="debt_amount" usageType="active" optype="continuous" />
<MiningField name="account_balance" usageType="active" optype="continuous" />
<MiningField name="dispute_risk" usageType="target" optype="categorical" />
</MiningSchema>
<Output>
<OutputField name="probability_1" optype="continuous" dataType="double" feature="probability" value="1" />
<OutputField name="probability_2" optype="continuous" dataType="double" feature="probability" value="2" />
<OutputField name="probability_3" optype="continuous" dataType="double" feature="probability" value="3" />
<OutputField name="probability_4" optype="continuous" dataType="double" feature="probability" value="4" />
<OutputField name="probability_5" optype="continuous" dataType="double" feature="probability" value="5" />
<OutputField name="predicted_dispute_risk" optype="categorical" dataType="integer" feature="predictedValue" />
</Output>
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