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Beginner question regarding train / test set
Learner IFirst of all: I am a total beginner in data science. For my university project, I need to create a process in rapidminer which predicts a customer satisfaction based on a survey. The dataset can be obtained from kaggle by searching for "Airline Passenger Satisfaction" by TJ Klein (cannot post links yet).
I get a train and a test set. I build my process based on the train set. so currently my process looks like this:
<?xml version="1.0" encoding="UTF-8"?><process version="10.3.001"> <context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="10.3.001" 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="SYSTEM"/>
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="10.3.001" expanded="true" height="68" name="Train Set" width="90" x="45" y="136">
<parameter key="repository_entry" value="train"/>
</operator>
<operator activated="true" class="subprocess" compatibility="10.3.001" expanded="true" height="82" name="Data Preparation" width="90" x="179" y="136">
<process expanded="true">
<operator activated="true" class="blending:select_attributes" compatibility="10.3.001" expanded="true" height="82" name="Select Attributes" width="90" x="45" y="34">
<parameter key="type" value="exclude attributes"/>
<parameter key="attribute_filter_type" value="a subset"/>
<parameter key="select_attribute" value=""/>
<parameter key="select_subset" value="att1␞id"/>
<parameter key="also_apply_to_special_attributes_(id,_label..)" value="false"/>
</operator>
<operator activated="true" class="filter_examples" compatibility="10.3.001" expanded="true" height="103" name="Filter Examples" width="90" x="246" y="34">
<parameter key="parameter_expression" value=""/>
<parameter key="condition_class" value="custom_filters"/>
<parameter key="invert_filter" value="true"/>
<list key="filters_list">
<parameter key="filters_entry_key" value="Arrival Delay in Minutes.is_missing."/>
</list>
<parameter key="filters_logic_and" value="true"/>
<parameter key="filters_check_metadata" value="true"/>
</operator>
<operator activated="true" class="blending:rename" compatibility="10.3.001" expanded="true" height="82" name="Rename" width="90" x="447" y="34">
<list key="rename attributes">
<parameter key="On-board service" value="boarding service"/>
</list>
<parameter key="from_attribute" value=""/>
<parameter key="to_attribute" value=""/>
</operator>
<operator activated="true" class="blending:set_role" compatibility="10.3.001" expanded="true" height="82" name="Set Role" width="90" x="648" y="34">
<list key="set_roles">
<parameter key="satisfaction" value="label"/>
</list>
</operator>
<connect from_port="in 1" to_op="Select Attributes" to_port="example set input"/>
<connect from_op="Select Attributes" from_port="example set output" to_op="Filter Examples" to_port="example set input"/>
<connect from_op="Filter Examples" from_port="example set output" to_op="Rename" to_port="example set input"/>
<connect from_op="Rename" 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_port="out 1"/>
<portSpacing port="source_in 1" spacing="0"/>
<portSpacing port="source_in 2" spacing="0"/>
<portSpacing port="sink_out 1" spacing="0"/>
<portSpacing port="sink_out 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="concurrency:cross_validation" compatibility="10.3.001" expanded="true" height="145" name="Cross Validation" width="90" x="380" y="136">
<parameter key="split_on_batch_attribute" value="false"/>
<parameter key="leave_one_out" value="false"/>
<parameter key="number_of_folds" value="9"/>
<parameter key="sampling_type" value="linear sampling"/>
<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="concurrency:parallel_decision_tree" compatibility="10.3.001" expanded="true" height="103" name="Decision Tree" width="90" x="112" y="34">
<parameter key="criterion" value="information_gain"/>
<parameter key="maximal_depth" value="20"/>
<parameter key="apply_pruning" value="true"/>
<parameter key="confidence" value="0.1"/>
<parameter key="apply_prepruning" value="true"/>
<parameter key="minimal_gain" value="0.01"/>
<parameter key="minimal_leaf_size" value="2"/>
<parameter key="minimal_size_for_split" value="4"/>
<parameter key="number_of_prepruning_alternatives" value="3"/>
</operator>
<connect from_port="training set" to_op="Decision Tree" to_port="training set"/>
<connect from_op="Decision Tree" from_port="model" to_port="model"/>
<connect from_op="Decision Tree" from_port="exampleSet" to_port="through 1"/>
<portSpacing port="source_training set" 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">
<operator activated="true" class="apply_model" compatibility="10.3.001" expanded="true" height="82" name="Apply Model" width="90" x="112" y="34">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_classification" compatibility="10.3.001" expanded="true" height="82" name="Performance" width="90" x="313" y="34">
<parameter key="main_criterion" value="first"/>
<parameter key="accuracy" value="true"/>
<parameter key="classification_error" value="true"/>
<parameter key="kappa" value="false"/>
<parameter key="weighted_mean_recall" value="false"/>
<parameter key="weighted_mean_precision" value="false"/>
<parameter key="spearman_rho" value="false"/>
<parameter key="kendall_tau" value="false"/>
<parameter key="absolute_error" value="false"/>
<parameter key="relative_error" value="false"/>
<parameter key="relative_error_lenient" value="false"/>
<parameter key="relative_error_strict" value="false"/>
<parameter key="normalized_absolute_error" value="false"/>
<parameter key="root_mean_squared_error" value="false"/>
<parameter key="root_relative_squared_error" value="false"/>
<parameter key="squared_error" value="false"/>
<parameter key="correlation" value="false"/>
<parameter key="squared_correlation" value="false"/>
<parameter key="cross-entropy" value="false"/>
<parameter key="margin" value="false"/>
<parameter key="soft_margin_loss" value="false"/>
<parameter key="logistic_loss" value="false"/>
<parameter key="skip_undefined_labels" value="true"/>
<parameter key="use_example_weights" value="true"/>
<list key="class_weights"/>
</operator>
<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="performance 1"/>
<connect from_op="Performance" from_port="example set" to_port="test set results"/>
<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_test set results" spacing="0"/>
<portSpacing port="sink_performance 1" spacing="0"/>
<portSpacing port="sink_performance 2" spacing="0"/>
</process>
</operator>
<connect from_op="Train Set" from_port="output" to_op="Data Preparation" to_port="in 1"/>
<connect from_op="Data Preparation" from_port="out 1" to_op="Cross Validation" to_port="example set"/>
<connect from_op="Cross Validation" from_port="example set" to_port="result 2"/>
<connect from_op="Cross Validation" from_port="test result set" to_port="result 1"/>
<connect from_op="Cross Validation" from_port="performance 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"/>
<portSpacing port="sink_result 5" spacing="0"/>
<portSpacing port="sink_result 6" spacing="0"/>
</process>
</operator>
</process>
The thing that now confuses me is, where do I use my test set? I don't really now where and I should use it - if I should use it at all. The test set is not unlabeled btw. As it says on kaggle, it was just splitted from the train set and represents 20% of all data.
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Answers
You are using the Cross-validation operator in your model.
This operator performs the training and validation process in you. Basically, the operator divides the data set into k subsets of equal size, then the operator retains one subset and trains the model on the other k-1 subsets. the process is repeated k times, with a different test subset selected each time.
best,
Cesar
I know how the cross validator works, and thats why I am confused. Do I still need to use the test set which I got in a seperate file, or can i ignore it as the cross validator already did the testing?
Regards
The cross-validation operator performed the tests as mentioned above. In this case, you can use other data sets for validation purposes by using the Apply Model operator.
Best,
Cesar