How to apply batch model to non batched data in rapidminer - batch-processing

Question

i have created a model using batched validation, is there a way to apply this model to non-batched data?
Here is the sample process I created:
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="7.0.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="7.0.001" expanded="true" name="Process">
<parameter key="logverbosity" value="init"/>
<parameter key="random_seed" value="2001"/>
<parameter key="send_mail" value="never"/>
<parameter key="notification_email" value=""/>
<parameter key="process_duration_for_mail" value="30"/>
<parameter key="encoding" value="SYSTEM"/>
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="7.0.001" expanded="true" height="68" name="Retrieve distmodel3" width="90" x="45" y="136">
<parameter key="repository_entry" value="../data/distmodel3"/>
</operator>
<operator activated="true" class="set_role" compatibility="7.0.001" expanded="true" height="82" name="Set Role" width="90" x="246" y="187">
<parameter key="attribute_name" value="batchid"/>
<parameter key="target_role" value="batch"/>
<list key="set_additional_roles">
<parameter key="Letter" value="label"/>
<parameter key="Frame" value="batch"/>
<parameter key="Feat1" value="regular"/>
<parameter key="Feat2" value="regular"/>
<parameter key="Feat3" value="regular"/>
<parameter key="Feat4" value="regular"/>
<parameter key="Feat5" value="regular"/>
<parameter key="Feat6" value="regular"/>
<parameter key="Feat7" value="regular"/>
<parameter key="Feat8" value="regular"/>
<parameter key="Gender" value="regular"/>
</list>
</operator>
<operator activated="true" class="batch_x_validation" compatibility="7.0.001" expanded="true" height="124" name="Validation" width="90" x="380" y="85">
<parameter key="create_complete_model" value="false"/>
<parameter key="average_performances_only" value="true"/>
<process expanded="true">
<operator activated="false" class="weka:W-J48" compatibility="7.0.000" expanded="true" height="82" name="W-J48" width="90" x="112" y="34">
<parameter key="U" value="true"/>
<parameter key="C" value="0.25"/>
<parameter key="M" value="2.0"/>
<parameter key="R" value="false"/>
<parameter key="B" value="true"/>
<parameter key="S" value="false"/>
<parameter key="L" value="false"/>
<parameter key="A" value="false"/>
</operator>
<operator activated="true" class="k_nn" compatibility="7.0.001" expanded="true" height="82" name="k-NN" width="90" x="112" y="187">
<parameter key="k" value="3"/>
<parameter key="weighted_vote" value="false"/>
<parameter key="measure_types" value="MixedMeasures"/>
<parameter key="mixed_measure" value="MixedEuclideanDistance"/>
<parameter key="nominal_measure" value="NominalDistance"/>
<parameter key="numerical_measure" value="EuclideanDistance"/>
<parameter key="divergence" value="GeneralizedIDivergence"/>
<parameter key="kernel_type" value="radial"/>
<parameter key="kernel_gamma" value="1.0"/>
<parameter key="kernel_sigma1" value="1.0"/>
<parameter key="kernel_sigma2" value="0.0"/>
<parameter key="kernel_sigma3" value="2.0"/>
<parameter key="kernel_degree" value="3.0"/>
<parameter key="kernel_shift" value="1.0"/>
<parameter key="kernel_a" value="1.0"/>
<parameter key="kernel_b" value="0.0"/>
</operator>
<connect from_port="training" to_op="k-NN" to_port="training set"/>
<connect from_op="k-NN" from_port="model" to_port="model"/>
<portSpacing port="source_training" 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="7.0.001" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
<list key="application_parameters"/>
<parameter key="create_view" value="false"/>
</operator>
<operator activated="true" class="performance_classification" compatibility="7.0.001" expanded="true" height="82" name="Performance" width="90" x="179" y="34">
<parameter key="main_criterion" value="first"/>
<parameter key="accuracy" value="true"/>
<parameter key="classification_error" value="true"/>
<parameter key="kappa" value="true"/>
<parameter key="weighted_mean_recall" value="false"/>
<parameter key="weighted_mean_precision" value="false"/>
<parameter key="spearman_rho" value="false"/>
<parameter key="kendall_tau" value="false"/>
<parameter key="absolute_error" value="false"/>
<parameter key="relative_error" value="false"/>
<parameter key="relative_error_lenient" value="false"/>
<parameter key="relative_error_strict" value="false"/>
<parameter key="normalized_absolute_error" value="false"/>
<parameter key="root_mean_squared_error" value="false"/>
<parameter key="root_relative_squared_error" value="false"/>
<parameter key="squared_error" value="false"/>
<parameter key="correlation" value="false"/>
<parameter key="squared_correlation" value="false"/>
<parameter key="cross-entropy" value="false"/>
<parameter key="margin" value="false"/>
<parameter key="soft_margin_loss" value="false"/>
<parameter key="logistic_loss" value="false"/>
<parameter key="skip_undefined_labels" value="true"/>
<parameter key="use_example_weights" 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="averagable 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="legacy:write_model" compatibility="7.0.001" expanded="true" height="68" name="Write Model" width="90" x="514" y="187">
<parameter key="model_file" value="C:\Users\Hans\Documents\ModelFile.mod"/>
<parameter key="overwrite_existing_file" value="true"/>
<parameter key="output_type" value="XML Zipped"/>
</operator>
<connect from_op="Retrieve distmodel3" from_port="output" to_op="Set Role" to_port="example set input"/>
<connect from_op="Set Role" from_port="example set output" to_op="Validation" to_port="training"/>
<connect from_op="Validation" from_port="model" to_op="Write Model" to_port="input"/>
<connect from_op="Validation" from_port="training" to_port="result 1"/>
<connect from_op="Validation" from_port="averagable 1" to_port="result 2"/>
<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"/>
</process>
</operator>
</process>
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Answer 1

The Batch Validation operator uses an attribute to split the training example set. Because this attribute is explicitly set to be of type batch, it is special and this means that it is not used when building the model; classification models use regular attributes to predict a class label. This means the model should work on an example set that does not contain the attribute with the batch role. If the model is used with an example set containing the batch attribute which is set to be regular, the performance will not depend on it (the model may not work at all - it depends on the model).