<?xml version="1.0" encoding="UTF-8" standalone="no"?><process version="5.0"> <context> <input/> <output/> <macros/> </context> <operator activated="true" class="process" expanded="true" name="Root"> <description>Using a logistic regression learner for a classification task of numerical data.</description> <process expanded="true" height="584" width="962"> <operator activated="true" class="retrieve" expanded="true" height="60" name="Retrieve" width="90" x="45" y="30"> <parameter key="repository_entry" value="../../data/Sonar"/> </operator> <operator activated="true" class="logistic_regression" expanded="true" height="94" name="MyKLRLearner" width="90" x="179" y="30"> <parameter key="calculate_weights" value="false"/> <parameter key="return_optimization_performance" value="false"/> </operator> <operator activated="true" class="apply_model" expanded="true" height="76" name="Apply Model" width="90" x="431" y="28"> <list key="application_parameters"/> </operator> <connect from_op="Retrieve" from_port="output" to_op="MyKLRLearner" to_port="training set"/> <connect from_op="MyKLRLearner" from_port="model" to_op="Apply Model" to_port="model"/> <connect from_op="MyKLRLearner" from_port="exampleSet" to_op="Apply Model" to_port="unlabelled data"/> <connect from_op="Apply Model" from_port="labelled data" to_port="result 2"/> <connect from_op="Apply Model" from_port="model" 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"/> <portSpacing port="sink_result 3" spacing="0"/> </process> </operator></process>
This "z" value is then evaluated in the equation 1/(1+e-z) in order to obtain the probabiliity that this particular instance will evaluate to "TRUE". Is this correct?
Bias 0.184weight_att 1 0.69att1 0.02linear predictor (mu) 0.19781/(1+exp(-mu)) 0.549289401
<?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="Root"> <description>Using a logistic regression learner for a classification task of numerical data.</description> <process expanded="true" height="298" width="614"> <operator activated="true" class="retrieve" expanded="true" height="60" name="Retrieve" width="90" x="45" y="30"> <parameter key="repository_entry" value="//Samples/data/Sonar"/> </operator> <operator activated="true" class="work_on_subset" expanded="true" height="94" name="Work on Subset" width="90" x="112" y="120"> <parameter key="attribute_filter_type" value="subset"/> <parameter key="attributes" value="attribute_1|class"/> <parameter key="include_special_attributes" value="true"/> <parameter key="keep_subset_only" value="true"/> <process expanded="true" height="298" width="614"> <connect from_port="exampleSet" to_port="example set"/> <portSpacing port="source_exampleSet" spacing="0"/> <portSpacing port="sink_example set" spacing="0"/> <portSpacing port="sink_through 1" spacing="0"/> <portSpacing port="sink_through 2" spacing="0"/> </process> </operator> <operator activated="true" class="logistic_regression" expanded="true" height="94" name="Logistic Regression" width="90" x="246" y="75"/> <operator activated="true" class="apply_model" expanded="true" height="76" name="Apply Model" width="90" x="447" y="165"> <list key="application_parameters"/> </operator> <connect from_op="Retrieve" from_port="output" to_op="Work on Subset" to_port="example set"/> <connect from_op="Work on Subset" from_port="example set" to_op="Logistic Regression" to_port="training set"/> <connect from_op="Work on Subset" from_port="through 1" to_port="result 4"/> <connect from_op="Logistic Regression" from_port="model" to_op="Apply Model" to_port="model"/> <connect from_op="Logistic Regression" from_port="weights" to_port="result 3"/> <connect from_op="Logistic Regression" from_port="exampleSet" to_op="Apply Model" to_port="unlabelled data"/> <connect from_op="Apply Model" from_port="labelled data" to_port="result 2"/> <connect from_op="Apply Model" from_port="model" 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"/> <portSpacing port="sink_result 3" spacing="0"/> <portSpacing port="sink_result 4" spacing="0"/> <portSpacing port="sink_result 5" spacing="0"/> </process> </operator></process>
<?xml version="1.0" encoding="UTF-8" standalone="no"?><process version="5.0"> <context> <input> <location/> </input> <output> <location/> <location/> <location/> </output> <macros/> </context> <operator activated="true" class="process" expanded="true" name="Root"> <process expanded="true" height="758" width="882"> <operator activated="true" class="retrieve" expanded="true" height="60" name="Retrieve" width="90" x="45" y="30"> <parameter key="repository_entry" value="//Samples/data/Sonar"/> </operator> <operator activated="true" class="support_vector_machine_libsvm" expanded="true" height="76" name="SVM" width="90" x="238" y="29"> <parameter key="kernel_type" value="poly"/> <list key="class_weights"/> </operator> <operator activated="true" class="retrieve" expanded="true" height="60" name="Retrieve (2)" width="90" x="179" y="210"> <parameter key="repository_entry" value="//Samples/data/Sonar"/> </operator> <operator activated="true" class="apply_model" expanded="true" height="76" name="Apply Model" width="90" x="447" y="210"> <list key="application_parameters"/> </operator> <connect from_op="Retrieve" from_port="output" to_op="SVM" to_port="training set"/> <connect from_op="SVM" from_port="model" to_op="Apply Model" to_port="model"/> <connect from_op="Retrieve (2)" from_port="output" to_op="Apply Model" to_port="unlabelled data"/> <connect from_op="Apply Model" from_port="labelled data" to_port="result 2"/> <connect from_op="Apply Model" from_port="model" 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"/> <portSpacing port="sink_result 3" spacing="0"/> </process> </operator></process>
Kernel ModelTotal number of Support Vectors: 159 Bias (offset): -1.191 w[attribute_1] = 23749.738 w[attribute_2] = 31592.323 w[attribute_3] = 35680.074 w[attribute_4] = 46113.371 w[attribute_5] = 58430.884 w[attribute_6] = 74797.426 w[attribute_7] = 86353.872 w[attribute_8] = 95989.628 w[attribute_9] = 129648.901 w[attribute_10] = 152098.800 w[attribute_11] = 179324.874 w[attribute_12] = 191024.717 w[attribute_13] = 200005.157 w[attribute_14] = 207625.943 ......w[attribute_58] = 6238.179 w[attribute_59] = 6269.692 w[attribute_60] = 4968.341 number of classes: 2 number of support vectors for class Rock: 78 number of support vectors for class Mine: 81
Dear Pavel,
welcome to the community!
It is technically possible to take the equation from RM and put it into RM. But the big question is - why do you want this?
A Model is always the connection of the preprocessing and the machine learning part itself. If you use a machine learning model on a table which is not prepeared in the very same way it will work but create wrong or unreasonable results.
Why dont you create a rm porcess: Read Excel -> Prepare Data -> Apply Model -> Write Excel
Best,
Martin
IMHO, the method that Martin proposes is probably the faster and better solution.
Import your data (by Excel or Database) do your training and scoring in RapidMiner, and at the end write out the predicted results to Excel. Going through the trouble of finding the weights in RapidMiner to then plug it into a Excel Logistic Regression model and crunching it there feels very time consuming.
However, you can extract the Logistic Regression operator weights by using a Weight to Data operator and then Write Excel.