how to convert polynomial to binomial

Hi, I have a data set with the column (IDKC WS(10)) of polynomial labels ("W","M", "I"). I use ExampleFilter to remove rows whose values are "I" so the column has two classes now. but Rapidminer thinks that the column is still polynomial so I cannot run logistic regression using the column as dependent variable. what should I do? Thanks!

<?xml version="1.0" encoding="UTF-8"?><process version="8.0.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="8.0.001" expanded="true" name="Process">
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="8.0.001" expanded="true" height="68" name="Retrieve Delete blank-Seoyeon’s MacBook Pro-4" width="90" x="112" y="85">
<parameter key="repository_entry" value="../data/Delete blank-Seoyeon’s MacBook Pro-4"/>
</operator>
<operator activated="true" class="filter_examples" compatibility="8.0.001" expanded="true" height="103" name="Filter Examples" width="90" x="112" y="187">
<list key="filters_list">
<parameter key="filters_entry_key" value="IDKC WS(10).does_not_equal.I"/>
</list>
</operator>
<operator activated="true" class="select_attributes" compatibility="8.0.001" expanded="true" height="82" name="Select Attributes" width="90" x="112" y="340">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attributes" value="IDKC_Ave_First Attempt_Opp1|IDPT_Ave_First Attempt_OPP1|ID_Ave_FirstAttempt_Opp1|KC_Ave_FirstAttempt_Opp1|PT_Ave_FirstAttempt_Opp1|IDKC WS(10)"/>
</operator>
<operator activated="true" class="set_role" compatibility="8.0.001" expanded="true" height="82" name="Set Role" width="90" x="112" y="493">
<parameter key="attribute_name" value="IDKC WS(10)"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="concurrency:cross_validation" compatibility="8.0.001" expanded="true" height="145" name="Cross Validation" width="90" x="447" y="136">
<parameter key="sampling_type" value="stratified sampling"/>
<process expanded="true">
<operator activated="true" class="h2o:logistic_regression" compatibility="7.6.001" expanded="true" height="124" name="Logistic Regression" width="90" x="112" y="34"/>
<connect from_port="training set" to_op="Logistic Regression" to_port="training set"/>
<connect from_op="Logistic Regression" from_port="model" to_port="model"/>
<portSpacing port="source_training set" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
<process expanded="true">
<operator activated="true" class="apply_model" compatibility="8.0.001" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_classification" compatibility="8.0.001" expanded="true" height="82" name="Performance" width="90" x="246" y="85">
<parameter key="classification_error" value="true"/>
<parameter key="kappa" value="true"/>
<parameter key="root_mean_squared_error" value="true"/>
<parameter key="correlation" value="true"/>
<list key="class_weights"/>
</operator>
<connect from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="performance 1"/>
<connect from_op="Performance" from_port="example set" to_port="test set results"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_test set results" spacing="0"/>
<portSpacing port="sink_performance 1" spacing="0"/>
<portSpacing port="sink_performance 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="apply_model" compatibility="8.0.001" expanded="true" height="82" name="Apply Model (2)" width="90" x="581" y="136">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_classification" compatibility="8.0.001" expanded="true" height="82" name="Performance (2)" width="90" x="782" y="136">
<parameter key="classification_error" value="true"/>
<parameter key="root_mean_squared_error" value="true"/>
<list key="class_weights"/>
</operator>
<connect from_op="Retrieve Delete blank-Seoyeon’s MacBook Pro-4" from_port="output" to_op="Filter Examples" to_port="example set input"/>
<connect from_op="Filter Examples" from_port="example set output" to_op="Select Attributes" to_port="example set input"/>
<connect from_op="Select Attributes" from_port="example set output" to_op="Set Role" to_port="example set input"/>
<connect from_op="Set Role" from_port="example set output" to_op="Cross Validation" to_port="example set"/>
<connect from_op="Cross Validation" from_port="model" to_op="Apply Model (2)" to_port="model"/>
<connect from_op="Cross Validation" from_port="example set" to_op="Apply Model (2)" to_port="unlabelled data"/>
<connect from_op="Apply Model (2)" from_port="labelled data" to_op="Performance (2)" to_port="labelled data"/>
<connect from_op="Apply Model (2)" from_port="model" to_port="result 2"/>
<connect from_op="Performance (2)" from_port="performance" to_port="result 1"/>
<connect from_op="Performance (2)" from_port="example set" to_port="result 3"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
<portSpacing port="sink_result 3" spacing="0"/>
<portSpacing port="sink_result 4" spacing="0"/>
</process>
</operator>
</process>