Does having a cross validation in your model have any impact?
Maven
Hello,
I'm wondering if having a cross validation's model input into an apply model with test and training data has a positive impact on the accuracy of your model? Or is cross validation only a way to see the performance of your model on your training data?
Here's an example of what I mean:
I'm wondering if having a cross validation's model input into an apply model with test and training data has a positive impact on the accuracy of your model? Or is cross validation only a way to see the performance of your model on your training data?
Here's an example of what I mean:
<?xml version="1.0" encoding="UTF-8"?><process version="9.2.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="9.2.001" 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="UTF-8"/>
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="9.2.001" expanded="true" height="68" name="Retrieve Titanic Training" width="90" x="45" y="34">
<parameter key="repository_entry" value="//Samples/data/Titanic Training"/>
</operator>
<operator activated="true" class="select_attributes" compatibility="9.2.001" expanded="true" height="82" name="Select Attributes" width="90" x="179" y="34">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="Survived"/>
<parameter key="attributes" value=""/>
<parameter key="use_except_expression" value="false"/>
<parameter key="value_type" value="attribute_value"/>
<parameter key="use_value_type_exception" value="false"/>
<parameter key="except_value_type" value="time"/>
<parameter key="block_type" value="attribute_block"/>
<parameter key="use_block_type_exception" value="false"/>
<parameter key="except_block_type" value="value_matrix_row_start"/>
<parameter key="invert_selection" value="true"/>
<parameter key="include_special_attributes" value="true"/>
</operator>
<operator activated="true" class="set_role" compatibility="9.2.001" expanded="true" height="82" name="Set Role" width="90" x="313" y="34">
<parameter key="attribute_name" value="Passenger Class"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="concurrency:cross_validation" compatibility="9.2.001" expanded="true" height="145" name="Cross Validation" width="90" x="447" y="34">
<parameter key="split_on_batch_attribute" value="false"/>
<parameter key="leave_one_out" value="false"/>
<parameter key="number_of_folds" value="10"/>
<parameter key="sampling_type" value="automatic"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
<parameter key="enable_parallel_execution" value="true"/>
<process expanded="true">
<operator activated="true" class="naive_bayes" compatibility="9.2.001" expanded="true" height="82" name="Naive Bayes (2)" width="90" x="112" y="34">
<parameter key="laplace_correction" value="true"/>
</operator>
<connect from_port="training set" to_op="Naive Bayes (2)" to_port="training set"/>
<connect from_op="Naive Bayes (2)" from_port="model" to_port="model"/>
<portSpacing port="source_training set" 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="9.2.001" expanded="true" height="82" name="Apply Model (2)" width="90" x="45" y="34">
<list key="application_parameters"/>
<parameter key="create_view" value="false"/>
</operator>
<operator activated="true" class="performance" compatibility="9.2.001" expanded="true" height="82" name="Performance" width="90" x="179" y="34">
<parameter key="use_example_weights" value="true"/>
</operator>
<connect from_port="model" to_op="Apply Model (2)" to_port="model"/>
<connect from_port="test set" to_op="Apply Model (2)" to_port="unlabelled data"/>
<connect from_op="Apply Model (2)" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="performance 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_test set results" spacing="0"/>
<portSpacing port="sink_performance 1" spacing="0"/>
<portSpacing port="sink_performance 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="retrieve" compatibility="9.2.001" expanded="true" height="68" name="Retrieve Titanic Unlabeled" width="90" x="313" y="187">
<parameter key="repository_entry" value="//Samples/data/Titanic Unlabeled"/>
</operator>
<operator activated="true" class="set_role" compatibility="9.2.001" expanded="true" height="82" name="Set Role (2)" width="90" x="447" y="187">
<parameter key="attribute_name" value="Passenger Class"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="apply_model" compatibility="9.2.001" expanded="true" height="82" name="Apply Model" width="90" x="648" y="34">
<list key="application_parameters"/>
<parameter key="create_view" value="false"/>
</operator>
<connect from_op="Retrieve Titanic Training" from_port="output" 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="Cross Validation" to_port="example set"/>
<connect from_op="Cross Validation" from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_op="Retrieve Titanic Unlabeled" from_port="output" to_op="Set Role (2)" to_port="example set input"/>
<connect from_op="Set Role (2)" from_port="example set output" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" 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>
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="9.2.001" 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="UTF-8"/>
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="9.2.001" expanded="true" height="68" name="Retrieve Titanic Training" width="90" x="45" y="34">
<parameter key="repository_entry" value="//Samples/data/Titanic Training"/>
</operator>
<operator activated="true" class="select_attributes" compatibility="9.2.001" expanded="true" height="82" name="Select Attributes" width="90" x="179" y="34">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="Survived"/>
<parameter key="attributes" value=""/>
<parameter key="use_except_expression" value="false"/>
<parameter key="value_type" value="attribute_value"/>
<parameter key="use_value_type_exception" value="false"/>
<parameter key="except_value_type" value="time"/>
<parameter key="block_type" value="attribute_block"/>
<parameter key="use_block_type_exception" value="false"/>
<parameter key="except_block_type" value="value_matrix_row_start"/>
<parameter key="invert_selection" value="true"/>
<parameter key="include_special_attributes" value="true"/>
</operator>
<operator activated="true" class="set_role" compatibility="9.2.001" expanded="true" height="82" name="Set Role" width="90" x="313" y="34">
<parameter key="attribute_name" value="Passenger Class"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="concurrency:cross_validation" compatibility="9.2.001" expanded="true" height="145" name="Cross Validation" width="90" x="447" y="34">
<parameter key="split_on_batch_attribute" value="false"/>
<parameter key="leave_one_out" value="false"/>
<parameter key="number_of_folds" value="10"/>
<parameter key="sampling_type" value="automatic"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
<parameter key="enable_parallel_execution" value="true"/>
<process expanded="true">
<operator activated="true" class="naive_bayes" compatibility="9.2.001" expanded="true" height="82" name="Naive Bayes (2)" width="90" x="112" y="34">
<parameter key="laplace_correction" value="true"/>
</operator>
<connect from_port="training set" to_op="Naive Bayes (2)" to_port="training set"/>
<connect from_op="Naive Bayes (2)" from_port="model" to_port="model"/>
<portSpacing port="source_training set" 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="9.2.001" expanded="true" height="82" name="Apply Model (2)" width="90" x="45" y="34">
<list key="application_parameters"/>
<parameter key="create_view" value="false"/>
</operator>
<operator activated="true" class="performance" compatibility="9.2.001" expanded="true" height="82" name="Performance" width="90" x="179" y="34">
<parameter key="use_example_weights" value="true"/>
</operator>
<connect from_port="model" to_op="Apply Model (2)" to_port="model"/>
<connect from_port="test set" to_op="Apply Model (2)" to_port="unlabelled data"/>
<connect from_op="Apply Model (2)" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="performance 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_test set results" spacing="0"/>
<portSpacing port="sink_performance 1" spacing="0"/>
<portSpacing port="sink_performance 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="retrieve" compatibility="9.2.001" expanded="true" height="68" name="Retrieve Titanic Unlabeled" width="90" x="313" y="187">
<parameter key="repository_entry" value="//Samples/data/Titanic Unlabeled"/>
</operator>
<operator activated="true" class="set_role" compatibility="9.2.001" expanded="true" height="82" name="Set Role (2)" width="90" x="447" y="187">
<parameter key="attribute_name" value="Passenger Class"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="apply_model" compatibility="9.2.001" expanded="true" height="82" name="Apply Model" width="90" x="648" y="34">
<list key="application_parameters"/>
<parameter key="create_view" value="false"/>
</operator>
<connect from_op="Retrieve Titanic Training" from_port="output" 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="Cross Validation" to_port="example set"/>
<connect from_op="Cross Validation" from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_op="Retrieve Titanic Unlabeled" from_port="output" to_op="Set Role (2)" to_port="example set input"/>
<connect from_op="Set Role (2)" from_port="example set output" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" 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>
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Best Answer
-
MartinLiebig
Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,507 
RM Data Scientist
Hi @Prentice ,cross validation is a method to estimate the true performance of your model on TESTING data (i.e unseen data). It does NOT change the mode which is generated.BR,Martin- Sr. Director Data Solutions, Altair RapidMiner -
Dortmund, Germany5
Answers