Do Not Use Certain Attributes in Prediction

New Altair Community Member
Updated by Jocelyn
Hi, I have attributes that are useful as information in a training and scored example set, but I do not want them to be used in my learning model. How do I set an attribute so it comes along for the ride as training or scored data in the examples, but is not used for learning? I don't see a target role that allows data to be consumed like a "comment" or such.
thanks
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The Set Role operator isn't just limited to the listed options. You can type whatever you like in there & make the field special.
Here's an example.
<?xml version="1.0" encoding="UTF-8"?><process version="7.6.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="7.6.001" expanded="true" name="Process">
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="7.6.001" expanded="true" height="68" name="Retrieve Golf" width="90" x="179" y="136">
<parameter key="repository_entry" value="//Samples/data/Golf"/>
</operator>
<operator activated="true" class="set_role" compatibility="7.6.001" expanded="true" height="82" name="Set Role" width="90" x="380" y="136">
<parameter key="attribute_name" value="Outlook"/>
<parameter key="target_role" value="comment1"/>
<list key="set_additional_roles">
<parameter key="Humidity" value="comment2"/>
</list>
<description align="center" color="transparent" colored="false" width="126">Set Roles of Outlook &amp; Humidity as special</description>
</operator>
<operator activated="true" class="multiply" compatibility="7.6.001" expanded="true" height="103" name="Multiply" width="90" x="514" y="161"/>
<operator activated="true" class="concurrency:loop_attributes" compatibility="7.6.001" expanded="true" height="82" name="Loop Attributes" width="90" x="648" y="238">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attributes" value="Outlook|Humidity"/>
<parameter key="include_special_attributes" value="true"/>
<parameter key="reuse_results" value="true"/>
<process expanded="true">
<operator activated="true" class="set_role" compatibility="7.6.001" expanded="true" height="82" name="Return Role" width="90" x="179" y="136">
<parameter key="attribute_name" value="%{loop_attribute}"/>
<list key="set_additional_roles"/>
</operator>
<connect from_port="input 1" to_op="Return Role" to_port="example set input"/>
<connect from_op="Return Role" 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>
<description align="center" color="transparent" colored="false" width="126">Return Outlook &amp; Humidity to normal.</description>
</operator>
<connect from_op="Retrieve Golf" from_port="output" 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_port="result 1"/>
<connect from_op="Multiply" from_port="output 2" to_op="Loop Attributes" to_port="input 1"/>
<connect from_op="Loop Attributes" from_port="output 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>
You would most likely need to split your data set prior to training and then join it back together afterwards, like this: