🎉Community Raffle - Win $25

An exclusive raffle opportunity for active members like you! Complete your profile, answer questions and get your first accepted badge to enter the raffle.

Join and Win

"Problem with Feature Selection"

vitalimario

Hi,

I am trying to perform Feature Selection, then apply the reduced subset of features to a J48 tree and finally present the tree to the output.

As you will see i have the 'create_complete_model option' checked
Unfortunately *all* tutorials of feature selection in RM use either SVM or NearestNeigbours models which both do not return an output!

Here is my setting :

<operator name="Root" class="Process" expanded="yes">
<operator name="CSVExampleSource" class="CSVExampleSource">
<parameter key="filename" value="c::\MyDocuments\score.csv"/>
<parameter key="label_name" value="class"/>
</operator>
<operator name="FeatureSelection" class="FeatureSelection" expanded="yes">
<parameter key="selection_direction" value="backward"/>
<operator name="SimpleValidation" class="SimpleValidation" expanded="yes">
<parameter key="create_complete_model" value="true"/>
<operator name="W-J48" class="W-J48">
</operator>
<operator name="ApplierChain" 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="ProcessLog" class="ProcessLog">
<list key="log">
<parameter key="Generation" value="operator.LibSVMLearner.value.applycount"/>
</list>
</operator>
</operator>
</operator>

What am i doing wrong ? ???

Find more posts tagged with

AI Studio

Feature Selection

Accepted answers

All comments

steffen

Hello

I am afraid I got you wrong, but:
The decision tree you create is used within the "performance measurement"-process to determine the (sub-)optimal set of features. If you want to create a Decision Tree with the best feature subset you must add something like this (at top level):


    <operator name="AttributeWeightSelection" class="AttributeWeightSelection">
    </operator>
    <operator name="W-J48 (2)" class="W-J48">
    </operator>

hope this was helpful,

Steffen

vitalimario

Hi Steffen,

Let me be more specific

Originally i would like to know whether the setup on the RM tutorial 12_WrapperValidation.xml can use a J48 decision tree (instead of JMySVMLearner and Regression Problem) and show the results (ie the Decision Tree) to the output : I changed the setup and i am not able to get the resulting Final tree (after feature selection) to the output :

Here is the final setup :

<operator name="Root" class="Process" expanded="yes">
<operator name="CSVExampleSource" class="CSVExampleSource">
<parameter key="filename" value="C:\test.csv"/>
<parameter key="label_name" value="class"/>
</operator>
<operator name="WrapperXValidation" class="WrapperXValidation" expanded="yes">
<parameter key="sampling_type" value="shuffled sampling"/>
<operator name="FeatureSelection" class="FeatureSelection" expanded="yes">
<operator name="FSXValidation" class="XValidation" expanded="yes">
<parameter key="sampling_type" value="shuffled sampling"/>
<operator name="W-J48" class="W-J48">
</operator>
<operator name="FSOperatorChain" class="OperatorChain" expanded="yes">
<operator name="FSModelApplier" class="ModelApplier">
<list key="application_parameters">
</list>
</operator>
<operator name="ClassificationPerformance" class="ClassificationPerformance">
<parameter key="accuracy" value="true"/>
<list key="class_weights">
</list>
<parameter key="classification_error" value="true"/>
<parameter key="kappa" value="true"/>
<parameter key="spearman_rho" value="true"/>
<parameter key="weighted_mean_precision" value="true"/>
<parameter key="weighted_mean_recall" value="true"/>
</operator>
<operator name="FSMinMaxWrapper" class="MinMaxWrapper">
<parameter key="minimum_weight" value="0.5"/>
</operator>
</operator>
</operator>
</operator>
<operator name="W-J48 (2)" class="W-J48">
</operator>
<operator name="OperatorChain" class="OperatorChain" expanded="yes">
<operator name="ModelApplier" class="ModelApplier">
<list key="application_parameters">
</list>
</operator>
<operator name="ClassificationPerformance (2)" class="ClassificationPerformance">
<parameter key="accuracy" value="true"/>
<list key="class_weights">
</list>
<parameter key="classification_error" value="true"/>
<parameter key="kappa" value="true"/>
<parameter key="weighted_mean_recall" value="true"/>
</operator>
</operator>
</operator>
</operator>

Thanks!

steffen

Hello again

Hm .. ok I think I got it now. Since there is no option to build a complete model, you cannot do it "in the same process".

You just have to repeat this part:

<operator name="Root" class="Process" expanded="yes">
    <operator name="FeatureSelection" class="FeatureSelection" expanded="no">
        <operator name="FSXValidation" class="XValidation" expanded="no">
            <parameter key="sampling_type"	value="shuffled sampling"/>
            <operator name="W-J48" class="W-J48">
            </operator>
            <operator name="FSOperatorChain" class="OperatorChain" expanded="yes">
                <operator name="FSModelApplier" class="ModelApplier">
                    <list key="application_parameters">
                    </list>
                </operator>
                <operator name="ClassificationPerformance" class="ClassificationPerformance">
                    <parameter key="accuracy"	value="true"/>
                    <list key="class_weights">
                    </list>
                    <parameter key="classification_error"	value="true"/>
                    <parameter key="kappa"	value="true"/>
                    <parameter key="spearman_rho"	value="true"/>
                    <parameter key="weighted_mean_precision"	value="true"/>
                    <parameter key="weighted_mean_recall"	value="true"/>
                </operator>
                <operator name="FSMinMaxWrapper" class="MinMaxWrapper">
                    <parameter key="minimum_weight"	value="0.5"/>
                </operator>
            </operator>
        </operator>
    </operator>
    <operator name="AttributeWeightSelection" class="AttributeWeightSelection">
    </operator>
    <operator name="W-J48 (2)" class="W-J48">
    </operator>
</operator>

because this is exactly what "build complete model" does/would do.

hope this was helpful,

Steffen

vitalimario

Dear Steffen,

It worked GREAT! Thanks again