Comparing results of two classificators

Ola

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?

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.