Validate data using historical data
aiguti
New Altair Community Member
Dear felows,
I have one historical data of part weight (peso bruto) and I would like to validate if one sample of parts is within the expected value (peso).
I do not know what is the right Model to be used. I tried LDA, Naive Bayes and others but it did not work.
here is the XML
I have one historical data of part weight (peso bruto) and I would like to validate if one sample of parts is within the expected value (peso).
I do not know what is the right Model to be used. I tried LDA, Naive Bayes and others but it did not work.
here is the XML
Thank you
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.3.005">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.3.005" expanded="true" name="Process">
<process expanded="true">
<operator activated="true" class="read_csv" compatibility="5.3.005" expanded="true" height="60" name="Read CSV" width="90" x="45" y="30">
<parameter key="csv_file" value="C:\Users\aiguti\Documents\kdd\peso - training.csv"/>
<parameter key="first_row_as_names" value="false"/>
<list key="annotations">
<parameter key="0" value="Name"/>
</list>
<parameter key="encoding" value="windows-1252"/>
<list key="data_set_meta_data_information">
<parameter key="0" value="Ecode.true.polynominal.attribute"/>
<parameter key="1" value="Peso Bruto.true.integer.attribute"/>
</list>
</operator>
<operator activated="true" class="read_csv" compatibility="5.3.005" expanded="true" height="60" name="Read CSV (2)" width="90" x="45" y="255">
<parameter key="csv_file" value="C:\Users\aiguti\Documents\kdd\peso - scoring.csv"/>
<parameter key="first_row_as_names" value="false"/>
<list key="annotations">
<parameter key="0" value="Name"/>
</list>
<parameter key="encoding" value="windows-1252"/>
<list key="data_set_meta_data_information">
<parameter key="0" value="ecode.true.polynominal.attribute"/>
<parameter key="1" value="Peso.true.integer.attribute"/>
</list>
</operator>
<operator activated="true" class="set_role" compatibility="5.3.005" expanded="true" height="76" name="Set Role" width="90" x="179" y="30">
<parameter key="name" value="Peso Bruto"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="vector_linear_regression" compatibility="5.3.005" expanded="true" height="76" name="Vector Linear Regression" width="90" x="380" y="30"/>
<operator activated="true" class="apply_model" compatibility="5.3.005" expanded="true" height="76" name="Apply Model" width="90" x="648" y="30">
<list key="application_parameters"/>
</operator>
<connect from_op="Read CSV" from_port="output" to_op="Set Role" to_port="example set input"/>
<connect from_op="Read CSV (2)" from_port="output" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Set Role" from_port="example set output" to_op="Vector Linear Regression" to_port="training set"/>
<connect from_op="Vector Linear Regression" from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_op="Apply Model" from_port="labelled data" to_port="result 1"/>
<connect from_op="Apply Model" from_port="model" 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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0
Answers
-
Hi aiguti,
with your process you train a model using a dataset called peso-training and later apply it to peso-scoring. So far this looks like a typical holdout strategy, you only need to add a "Performance" operator to compute some performance values. In order to recommend a suitable learner it maybe helpful if you could tell me more about your data and what exactly you want to achieve.
Cheers,
Helge0