All the value of Performance vector in Cross Validation are zero

salmaardelia_salmaardelia_ Member Posts: 1 Newbie
Hiii, im new on Rapid Miner; 

Im doing sentimen analysis with Naive Bayes alghoritm.
When i use "Apply Model", the model can show prediction.But when i use "Cross Validation" and i run it, the prediction vector show that all the value are zero. Why? 

Please help me..

Regards

Answers

  • rjones13rjones13 Member Posts: 128 Unicorn
    Hi @salmaardelia_,

    Would it be possible for you to post the data and models as well? Just as testing from my side it seems to work fine (attached simple example below).

    Best,

    Roland

    <?xml version="1.0" encoding="UTF-8"?><process version="10.3.000">
    
    <context>
    <input/>
    <output/>
    <macros/>
    </context>
    <operator activated="true" class="process" compatibility="10.3.000" expanded="true" name="Process" origin="GENERATED_TUTORIAL">
    <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" breakpoints="after" class="retrieve" compatibility="10.3.000" expanded="true" height="68" name="Retrieve Iris" origin="GENERATED_TUTORIAL" width="90" x="112" y="136">
    <parameter key="repository_entry" value="//Samples/data/Iris"/>
    </operator>
    <operator activated="true" class="concurrency:cross_validation" compatibility="10.3.000" expanded="true" height="145" name="Cross Validation" width="90" x="313" y="136">
    <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="10.3.000" expanded="true" height="82" name="Naive Bayes" width="90" x="45" y="34">
    <parameter key="laplace_correction" value="true"/>
    </operator>
    <connect from_port="training set" to_op="Naive Bayes" to_port="training set"/>
    <connect from_op="Naive Bayes" 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="10.3.000" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
    <list key="application_parameters"/>
    </operator>
    <operator activated="true" class="performance_classification" compatibility="10.3.000" expanded="true" height="82" name="Performance" width="90" x="179" 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="true"/>
    <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="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="Retrieve Iris" from_port="output" to_op="Cross Validation" to_port="example set"/>
    <connect from_op="Cross Validation" from_port="model" to_port="result 1"/>
    <connect from_op="Cross Validation" from_port="test result set" to_port="result 3"/>
    <connect from_op="Cross Validation" from_port="performance 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"/>
    <portSpacing port="sink_result 4" spacing="0"/>
    </process>
    </operator>
    </process>
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