Hi,
I would like to ask for help to build another prediction model, using SVM or other it is fine.
<?xml version="1.0" encoding="UTF-8"?><process version="8.2.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="6.0.002" expanded="true" name="Root">
<parameter key="random_seed" value="1969"/>
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="8.2.001" expanded="true" height="68" name="Retrieve Polynomial" width="90" x="45" y="34">
<parameter key="repository_entry" value="//Samples/data/Polynomial"/>
</operator>
<operator activated="true" class="generate_attributes" compatibility="8.2.001" expanded="true" height="82" name="Generate Attributes" width="90" x="179" y="34">
<list key="function_descriptions">
<parameter key="newval" value="a1"/>
</list>
</operator>
<operator activated="true" breakpoints="after" class="transpose" compatibility="8.2.001" expanded="true" height="82" name="Transpose" width="90" x="313" y="34"/>
<operator activated="true" class="concurrency:loop_attributes" compatibility="8.2.001" expanded="true" height="82" name="Loop Attributes" width="90" x="447" y="34">
<parameter key="attribute_filter_type" value="regular_expression"/>
<parameter key="attributes" value="|cluster"/>
<parameter key="regular_expression" value="newval.*"/>
<process expanded="true">
<operator activated="true" class="set_role" compatibility="8.2.001" expanded="true" height="82" name="Set Role" width="90" x="45" y="34">
<parameter key="attribute_name" value="%{loop_attribute}"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="multiply" compatibility="8.2.001" expanded="true" height="103" name="Multiply" width="90" x="179" y="34"/>
<operator activated="true" class="support_vector_machine" compatibility="8.2.001" expanded="true" height="124" name="SVM" width="90" x="514" y="34"/>
<operator activated="true" class="apply_model" compatibility="8.2.001" expanded="true" height="82" name="Apply Model" width="90" x="380" y="289">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="select_attributes" compatibility="8.2.001" expanded="true" height="82" name="Select Attributes (2)" width="90" x="715" y="85">
<parameter key="attribute_filter_type" value="regular_expression"/>
<parameter key="regular_expression" value="newval.*"/>
<parameter key="invert_selection" value="true"/>
<parameter key="include_special_attributes" value="true"/>
</operator>
<connect from_port="input 1" to_op="Set Role" to_port="example set input"/>
<connect from_op="Set Role" from_port="example set output" to_op="Multiply" to_port="input"/>
<connect from_op="Multiply" from_port="output 1" to_op="SVM" to_port="training set"/>
<connect from_op="Multiply" from_port="output 2" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="SVM" from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Select Attributes (2)" to_port="example set input"/>
<connect from_op="Select Attributes (2)" from_port="example set output" to_port="output 1"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="source_input 2" spacing="0"/>
<portSpacing port="sink_output 1" spacing="0"/>
<portSpacing port="sink_output 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="append" compatibility="8.2.001" expanded="true" height="82" name="Append" width="90" x="648" y="34"/>
<connect from_op="Retrieve Polynomial" from_port="output" to_op="Generate Attributes" to_port="example set input"/>
<connect from_op="Generate Attributes" from_port="example set output" to_op="Transpose" to_port="example set input"/>
<connect from_op="Transpose" from_port="example set output" to_op="Loop Attributes" to_port="input 1"/>
<connect from_op="Loop Attributes" from_port="output 1" to_op="Append" to_port="example set 1"/>
<connect from_op="Append" from_port="merged set" to_port="result 1"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
</process>
</operator>
</process>
Above is the process i am using currently. As i understood SVM learn operator, it will create a model based on the row behaviour of the data set. What I need to is a model based on the behaviour of the column of data set. I tried to transpose my data but that would make me lose the label which I need for prediction. So for my sample process above, I need the predicted result for att_201 based on the behaviour of the data set before the first transpose.
