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"No accuracy matrix in SVM"
Contributor I
Hi. I have a question about SVM in rapid miner. I dont know why svm couldn't create accuracy matrix as k-nn. it just shows root mean square error. what should I do?
I'm working on Text categorization and I have 3 columns. Title,Body and label. Body and title are text and label is numeric.
I used:
read database->process document->select attribute->set role(set label column as label)-> x-validation
I'm working on Text categorization and I have 3 columns. Title,Body and label. Body and title are text and label is numeric.
I used:
read database->process document->select attribute->set role(set label column as label)-> x-validation
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.2.000">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.2.000"
expanded="true" name="Process">
<parameter key="parallelize_main_process" value="true"/>
<process expanded="true" height="701" width="685">
<operator activated="true" class="read_database"
compatibility="5.2.000" expanded="true" height="60" name="Read
Database" width="90" x="45" y="30">
<parameter key="connection" value="ISNA_11News_Normalized"/>
<parameter key="query" value="SELECT "Title",
"Body", "Label" FROM
"dbo"."nws_full_corpus""/>
<parameter key="prepare_statement" value="true"/>
<enumeration key="parameters"/>
</operator>
<operator activated="true"
class="text:process_document_from_data" compatibility="5.1.004"
expanded="true" height="76" name="Process Documents from Data"
width="90" x="179" y="30">
<parameter key="prune_method" value="absolute"/>
<parameter key="prune_below_absolute" value="2"/>
<parameter key="prune_above_absolute" value="9999"/>
<list key="specify_weights"/>
<process expanded="true" height="298" width="671">
<operator activated="true" class="text:transform_cases"
compatibility="5.1.004" expanded="true" height="60" name="Transform
Cases" width="90" x="45" y="30"/>
<operator activated="true" class="text:tokenize"
compatibility="5.1.004" expanded="true" height="60" name="Tokenize"
width="90" x="45" y="165"/>
<operator activated="true" class="text:filter_by_length"
compatibility="5.1.004" expanded="true" height="60" name="Filter
Tokens (by Length)" width="90" x="179" y="120">
<parameter key="min_chars" value="2"/>
<parameter key="max_chars" value="99999"/>
</operator>
<connect from_port="document" to_op="Transform Cases"
to_port="document"/>
<connect from_op="Transform Cases" from_port="document"
to_op="Tokenize" to_port="document"/>
<connect from_op="Tokenize" from_port="document"
to_op="Filter Tokens (by Length)" to_port="document"/>
<connect from_op="Filter Tokens (by Length)"
from_port="document" to_port="document 1"/>
<portSpacing port="source_document" spacing="0"/>
<portSpacing port="sink_document 1" spacing="0"/>
<portSpacing port="sink_document 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="select_attributes"
compatibility="5.2.000" expanded="true" height="76" name="Select
Attributes" width="90" x="313" y="30">
<parameter key="attribute_filter_type" value="no_missing_values"/>
<parameter key="attribute" value="Label"/>
</operator>
<operator activated="true" class="set_role"
compatibility="5.2.000" expanded="true" height="76" name="Set Role"
width="90" x="447" y="30">
<parameter key="name" value="Label"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="x_validation"
compatibility="5.2.000" expanded="true" height="112" name="Validation"
width="90" x="514" y="165">
<parameter key="number_of_validations" value="2"/>
<parameter key="sampling_type" value="shuffled sampling"/>
<process expanded="true" height="332" width="346">
<operator activated="true" class="support_vector_machine"
compatibility="5.2.000" expanded="true" height="112" name="SVM (3)"
width="90" x="179" y="120"/>
<connect from_port="training" to_op="SVM (3)" to_port="training set"/>
<connect from_op="SVM (3)" from_port="model" to_port="model"/>
<connect from_op="SVM (3)" from_port="estimated performance"
to_port="through 1"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
<portSpacing port="sink_through 2" spacing="0"/>
</process>
<process expanded="true" height="298" width="310">
<operator activated="true" class="apply_model"
compatibility="5.2.000" expanded="true" height="76" name="Apply Model"
width="90" x="45" y="30">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance"
compatibility="5.2.000" expanded="true" height="76" name="Performance"
width="90" x="179" y="30"/>
<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="source_through 2" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<connect from_op="Read Database" from_port="output"
to_op="Process Documents from Data" to_port="example set"/>
<connect from_op="Process Documents from Data"
from_port="example set" 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="Validation" to_port="training"/>
<connect from_op="Validation" from_port="model" to_port="result 1"/>
<connect from_op="Validation" from_port="training" to_port="result 2"/>
<connect from_op="Validation" from_port="averagable 1"
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>
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Answers
I see you are using the automatic performance chooser, and it seems not to please ! All is not lost however, you can pick exactly what you want using the 'Performance ( Classification )' operator. Hope that does the trick.
Good weekend!
I want to try it, but performance (classification) doesn't work with integer label. RM offered use discretize, i did. but it says now label and prediction must be of the same , but are nominal and integer.
If you are doing classification you should convert the Label attribute type to nominal. If you work your way through 'help->Rapidminer tutorial' you'll see why the discretize operator is inappropriate.
Good luck.
Try using Bayes, or another learner that takes nominal attributes, like this