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How to nest/compare multiple learners?

delendelen Member Posts: 4 Contributor I
edited November 2018 in Help
I want to nest and compare 3 different learners such as ANN, DT and LR for a classification task. I want to use 10-fold CV. How can I do that? Do I need to do the XValidation separately for each algorithm, or is there a way to nest them into a meta block?




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    IngoRMIngoRM Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, Community Manager, RMResearcher, Member, University Professor Posts: 1,751 RM Founder

    you can combine the operator OperatorSelector with a parameter iteration like in the following process:

    <operator name="Root" class="Process" expanded="yes">
        <operator name="ExampleSetGenerator" class="ExampleSetGenerator">
            <parameter key="target_function" value="sum classification"/>
        <operator name="ParameterIteration" class="ParameterIteration" expanded="yes">
            <list key="parameters">
              <parameter key="OperatorSelector.select_which" value="[1.0;3.0;2;linear]"/>
            <parameter key="keep_output" value="true"/>
            <operator name="XValidation" class="XValidation" expanded="yes">
                <operator name="OperatorSelector" class="OperatorSelector" expanded="yes">
                    <parameter key="select_which" value="3"/>
                    <operator name="DecisionTree" class="DecisionTree">
                    <operator name="LinearRegression" class="LinearRegression">
                    <operator name="JMySVMLearner" class="JMySVMLearner">
                <operator name="OperatorChain" class="OperatorChain" expanded="yes">
                    <operator name="ModelApplier" class="ModelApplier">
                        <list key="application_parameters">
                    <operator name="Performance" class="Performance">
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