Hi guys,
thanks for the great work. I really like the new user-friendly interface. Its really a great step forward for non experts in data mining to use your tool.
I am trying to train two neural networks (hopefully the standard neural net is backpropagation and RBF from weka). I tried to adopt the online tutorial / video for measuring performance using cross validation.
I have several questions to that output which is presented in the result workspace.
1.
Although I only have two X-validation processes I have 3 tabs PerformanceVector results. And I am not sure why it is 3 and not 2. Can you help me with that? I am using only the gui for the process building and was thinking I created two exact copies of the cross validation processes where I only substituted the learner.
2. The performance Vector tab says
"root_mean_squared_error: 5.810 +/- 1.100 (mikro: 5.905 +/- 0.000)"
Which I think is pretty high for the prediction error.... anyway what does the does the information in the brackets mean? (Sorry I am not an expert in learning machines and not familiar with the notation)
In the log file it appears a strange warning: "Feb 28, 2010 7:54:11 PM WARNING: Caught exception in concurrent execution of Perf RBF (inner) (Performance (Regression)): com.rapidminer.operator.UserError: Input example set does not have a predicted label attribute"
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.0">
<context>
<input>
<location/>
</input>
<output>
<location/>
<location/>
<location/>
<location/>
<location/>
</output>
<macros/>
</context>
<operator activated="true" class="process" expanded="true" name="Process">
<process expanded="true" height="836" width="1304">
<operator activated="true" class="read_excel" expanded="true" height="60" name="Read Excel" width="90" x="45" y="75">
<parameter key="excel_file" value="C:\Users\Seb\Documents\VersuchsplanVoids2007.xls"/>
<parameter key="sheet_number" value="3"/>
</operator>
<operator activated="true" class="select_attributes" expanded="true" height="76" name="Select Attributes" width="90" x="112" y="165">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attributes" value="SolderPasteA|SolderPasteC|SolderPasteD|Wetting paste height|Wetting inner area mean|Wetting outer area|Alloy compound405|BGA Void mean|Soldering_Con|Soldering_O2|Soldering_Vac|Soldering_Vap"/>
</operator>
<operator activated="true" class="set_role" expanded="true" height="76" name="Set Role" width="90" x="179" y="300">
<parameter key="name" value="BGA Void mean"/>
<parameter key="target_role" value="label"/>
</operator>
<operator activated="true" class="filter_examples" expanded="true" height="76" name="Filter Examples" width="90" x="313" y="435">
<parameter key="condition_class" value="missing_labels"/>
<parameter key="invert_filter" value="true"/>
</operator>
<operator activated="true" class="multiply" expanded="true" height="94" name="Multiply" width="90" x="447" y="480"/>
<operator activated="true" class="x_validation" expanded="true" height="112" name="X-BPNN" width="90" x="715" y="615">
<parameter key="average_performances_only" value="false"/>
<parameter key="sampling_type" value="shuffled sampling"/>
<parameter key="parallelize_training" value="true"/>
<parameter key="parallelize_testing" value="true"/>
<process expanded="true" height="559" width="487">
<operator activated="true" class="replace_missing_values" expanded="true" height="94" name="Replace Missing Values" width="90" x="76" y="185">
<parameter key="include_special_attributes" value="true"/>
<parameter key="default" value="none"/>
<list key="columns"/>
</operator>
<operator activated="true" class="neural_net" expanded="true" height="76" name="Neural Net" width="90" x="246" y="165">
<list key="hidden_layers">
<parameter key="H1" value="15"/>
<parameter key="H2" value="5"/>
</list>
<parameter key="learning_rate" value="0.2"/>
<parameter key="momentum" value="0.4"/>
</operator>
<connect from_port="training" to_op="Replace Missing Values" to_port="example set input"/>
<connect from_op="Replace Missing Values" from_port="example set output" to_op="Neural Net" to_port="training set"/>
<connect from_op="Neural Net" from_port="model" to_port="model"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
<process expanded="true" height="559" width="487">
<operator activated="true" class="apply_model" expanded="true" height="76" name="Apply Model" width="90" x="112" y="75">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_regression" expanded="true" height="76" name="Perf BP (inner)" width="90" x="313" y="30">
<parameter key="main_criterion" value="root_mean_squared_error"/>
<parameter key="absolute_error" value="true"/>
<parameter key="correlation" value="true"/>
<parameter key="use_example_weights" value="false"/>
</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="Perf BP (inner)" to_port="labelled data"/>
<connect from_op="Perf BP (inner)" 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="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="x_validation" expanded="true" height="112" name="X-RBF" width="90" x="1050" y="300">
<parameter key="average_performances_only" value="false"/>
<parameter key="sampling_type" value="shuffled sampling"/>
<parameter key="parallelize_training" value="true"/>
<parameter key="parallelize_testing" value="true"/>
<process expanded="true" height="559" width="487">
<operator activated="true" class="replace_missing_values" expanded="true" height="94" name="Replace Missing Values (2)" width="90" x="45" y="30">
<parameter key="include_special_attributes" value="true"/>
<parameter key="default" value="none"/>
<list key="columns"/>
</operator>
<operator activated="true" class="weka:W-RBFNetwork" expanded="true" height="76" name="W-RBFNetwork" width="90" x="246" y="30">
<parameter key="B" value="15.0"/>
<parameter key="W" value="0.3"/>
</operator>
<connect from_port="training" to_op="Replace Missing Values (2)" to_port="example set input"/>
<connect from_op="Replace Missing Values (2)" from_port="example set output" to_op="W-RBFNetwork" to_port="training set"/>
<connect from_op="W-RBFNetwork" from_port="model" to_port="model"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
<process expanded="true" height="559" width="487">
<operator activated="true" class="apply_model" expanded="true" height="76" name="Apply Model (2)" width="90" x="45" y="30">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_regression" expanded="true" height="76" name="Perf RBF (inner)" width="90" x="253" y="30">
<parameter key="main_criterion" value="root_mean_squared_error"/>
<parameter key="absolute_error" value="true"/>
<parameter key="correlation" value="true"/>
<parameter key="use_example_weights" value="false"/>
</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="Perf RBF (inner)" to_port="labelled data"/>
<connect from_op="Perf RBF (inner)" 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="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<connect from_op="Read Excel" 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="Filter Examples" to_port="example set input"/>
<connect from_op="Filter Examples" from_port="example set output" to_op="Multiply" to_port="input"/>
<connect from_op="Multiply" from_port="output 1" to_op="X-RBF" to_port="training"/>
<connect from_op="Multiply" from_port="output 2" to_op="X-BPNN" to_port="training"/>
<connect from_op="X-BPNN" from_port="model" to_port="result 3"/>
<connect from_op="X-BPNN" from_port="averagable 1" to_port="result 4"/>
<connect from_op="X-RBF" from_port="model" to_port="result 1"/>
<connect from_op="X-RBF" from_port="averagable 1" to_port="result 2"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="270"/>
<portSpacing port="sink_result 2" spacing="18"/>
<portSpacing port="sink_result 3" spacing="108"/>
<portSpacing port="sink_result 4" spacing="0"/>
<portSpacing port="sink_result 5" spacing="0"/>
</process>
</operator>
</process>
