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Comparing results of two classificators

I'm just beginning to use RM and find that I like it a lot.

Most of the time I use mining for classification. How do I run, say a Logistic Regression and an SVM on the same dataset and compare the results? I know there is an ROCComparator, but what would the chain need to look like?

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haddock

HI, and welcome to the RapidMiner refuge!

The answer to your question is like this ....

<operator name="Root" class="Process" expanded="yes">
    <parameter key="logverbosity"	value="warning"/>
    <parameter key="random_seed"	value="2000"/>
    <operator name="ExampleSetGenerator" class="ExampleSetGenerator">
        <parameter key="target_function"	value="random dots classification"/>
        <parameter key="number_examples"	value="500"/>
        <parameter key="number_of_attributes"	value="2"/>
        <parameter key="attributes_lower_bound"	value="0.0"/>
        <parameter key="attributes_upper_bound"	value="25.0"/>
    </operator>
    <operator name="ROCComparator" class="ROCComparator" expanded="yes">
        <operator name="LibSVMLearner" class="LibSVMLearner">
            <parameter key="gamma"	value="1.0"/>
            <list key="class_weights">
            </list>
        </operator>
        <operator name="LinearRegression" class="LinearRegression">
        </operator>
    </operator>
</operator>

Pretty handy, so thanks for bringing it to our attention.

Ola

Thanks, that was brilliant.

Now I just need to combine this with cross validation and I'm set to go. Any thoughts on that?

haddock

Hi again,

Here's some code to do that sort of stuff....

<operator name="Root" class="Process" expanded="yes">
    <parameter key="logverbosity"	value="warning"/>
    <parameter key="random_seed"	value="2000"/>
    <operator name="ExampleSetGenerator" class="ExampleSetGenerator">
        <parameter key="target_function"	value="random dots classification"/>
        <parameter key="number_examples"	value="500"/>
        <parameter key="number_of_attributes"	value="2"/>
        <parameter key="attributes_lower_bound"	value="0.0"/>
        <parameter key="attributes_upper_bound"	value="25.0"/>
    </operator>
    <operator name="XValidation" class="XValidation" expanded="no">
        <parameter key="keep_example_set"	value="true"/>
        <parameter key="create_complete_model"	value="true"/>
        <parameter key="sampling_type"	value="shuffled sampling"/>
        <operator name="LibSVMLearner" class="LibSVMLearner">
            <parameter key="keep_example_set"	value="true"/>
            <parameter key="gamma"	value="1.0"/>
            <list key="class_weights">
            </list>
        </operator>
        <operator name="OperatorChain" class="OperatorChain" expanded="yes">
            <operator name="ModelApplier" class="ModelApplier">
                <list key="application_parameters">
                </list>
            </operator>
            <operator name="Performance" class="Performance">
            </operator>
        </operator>
    </operator>
    <operator name="ModelWriter" class="ModelWriter">
        <parameter key="model_file"	value="SVM"/>
    </operator>
    <operator name="XValidation (2)" class="XValidation" expanded="no">
        <parameter key="keep_example_set"	value="true"/>
        <parameter key="create_complete_model"	value="true"/>
        <parameter key="sampling_type"	value="shuffled sampling"/>
        <operator name="LinearRegression" class="LinearRegression">
        </operator>
        <operator name="OperatorChain (2)" class="OperatorChain" expanded="yes">
            <operator name="ModelApplier (2)" class="ModelApplier">
                <list key="application_parameters">
                </list>
            </operator>
            <operator name="Performance (2)" class="Performance">
            </operator>
        </operator>
    </operator>
    <operator name="ModelWriter (2)" class="ModelWriter">
        <parameter key="model_file"	value="LR"/>
    </operator>
    <operator name="ROCComparator" class="ROCComparator" expanded="yes">
        <operator name="SVM" class="ModelLoader">
            <parameter key="model_file"	value="SVM"/>
        </operator>
        <operator name="LR" class="ModelLoader">
            <parameter key="model_file"	value="LR"/>
        </operator>
    </operator>
</operator>

I've just bashed this together rather swiftly ( a big plus for RM ), and wiser heads than mine can do it much better, but you get the idea.

Have fun improving it!

Ola

Thank you! Very helpful.