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Learner III Eric005
Learner III

Making Label an Attribute - Decision Tree Process

HI All,


I'm currently working on a presentation piece using time series data for a binary classifier of stock market direction. I generate a custom attribute that makes a True/False indication (Up/Down) of a forward market price (this is under the column as Label2) using actual forward data in the series, and then my standard label attribute is the predicted value through a boosted decision tree.  Here is my question, when I select attributes as a final step going into the validation I will select the market date and market data, and this generally produces about a 74% accuracy.  If I also select the label as an attribute it then produces a 98% accuracy of prediction (which to me is absurd).  So I'm trying to understand the mechanics of what makes listing the label as an attribute have such a radical change in predictions - is the decision tree using previous predictions through the windowing function to influence forward predictions in a sort of looping system?  Does any of this make sense? 

Feedback welcome.  XML below.   

 

Thanks!

Eric

 

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4 REPLIES
Highlighted
RM Certified Expert
RM Certified Expert

Re: Making Label an Attribute - Decision Tree Process

I am sure Mr Ott will have a lot more to add than I on this.  He has a series of tutorials on exactly this.  

http://www.neuralmarkettrends.com/building-an-ai-financial-market-model-lesson-i/

 

A few really quick things from a short glance. 

Use sliding window validation for your time series, otherwise inside the XVal you are using examples from the future to predict the past. 

Where you set your other labels to regular, you can actually use Set Role to set the roles as Label1, Label2, etc.... they don't need to be regular if you don't want your model to use them. 

-- Training, Consulting, Sales in China, Hong Kong & Taiwan --
www.RapidMinerChina.com
Learner III Eric005
Learner III

Re: Making Label an Attribute - Decision Tree Process

Thank you for the Reply and Assistance.  

 

I realized the target attribute which generates the binary control was not correct, I have now corrected this in a subsequent arrangement.  

In this case, when I set a role (Set Role (2)) for the label to also be a regular attribute it, it again improves the accuracy.  

 

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<operator activated="false" class="neural_net" compatibility="7.4.000" expanded="true" height="82" name="Neural Net" width="90" x="782" y="391">
<list key="hidden_layers"/>
<parameter key="decay" value="true"/>
</operator>
<operator activated="false" class="bagging" compatibility="7.4.000" expanded="true" height="82" name="Bagging" width="90" x="782" y="493">
<process expanded="true">
<portSpacing port="source_training set" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
</process>
</operator>
<operator activated="true" class="retrieve" compatibility="7.4.000" expanded="true" height="68" name="Retrieve Date_NDX_SPX_VIX_RUT_DJX_HOLC Data (2)" width="90" x="45" y="34">
<parameter key="repository_entry" value="../Date_NDX_SPX_VIX_RUT_DJX_HOLC Data"/>
</operator>
<operator activated="true" class="select_attributes" compatibility="7.4.000" expanded="true" height="82" name="Select Attributes" width="90" x="179" y="34">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attributes" value="SPX Close|SPX High|SPX Low|SPX Open|Date"/>
</operator>
<operator activated="true" class="set_role" compatibility="7.4.000" expanded="true" height="82" name="Set Role" width="90" x="313" y="34">
<parameter key="attribute_name" value="SPX Close"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles">
<parameter key="Date" value="id"/>
<parameter key="SPX Close" value="label"/>
<parameter key="SPX Close" value="regular"/>
</list>
</operator>
<operator activated="true" class="series:windowing" compatibility="7.4.000" expanded="true" height="82" name="Windowing" width="90" x="447" y="34">
<parameter key="window_size" value="10"/>
<parameter key="create_label" value="true"/>
<parameter key="label_attribute" value="SPX Close"/>
<parameter key="label_dimension" value="0"/>
<parameter key="horizon" value="5"/>
</operator>
<operator activated="true" class="generate_attributes" compatibility="7.4.000" expanded="true" height="82" name="Generate Attributes" width="90" x="45" y="187">
<list key="function_descriptions">
<parameter key="BinaryForward" value="if(label&gt;[SPX Close-0],TRUE,FALSE)"/>
<parameter key="SPXClose2" value="[SPX Close-0]"/>
</list>
</operator>
<operator activated="true" class="set_role" compatibility="7.4.000" expanded="true" height="82" name="Set Role (2)" width="90" x="179" y="187">
<parameter key="attribute_name" value="BinaryForward"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles">
<parameter key="label" value="regular"/>
</list>
</operator>
<operator activated="true" class="quantx1:security_return_operator" compatibility="1.0.006" expanded="true" height="68" name="Differencing" width="90" x="313" y="187">
<parameter key="Price columns" value="SPX Close-0|SPX Close-1|SPX Close-2|SPX Close-3|SPX Close-4|SPX Close-5|SPX Close-6|SPX Close-7|SPX Close-8|SPX Close-9|SPX High-0|SPX High-1|SPX High-2|SPX High-3|SPX High-4|SPX High-5|SPX High-6|SPX High-7|SPX High-8|SPX High-9|SPX Low-0|SPX Low-1|SPX Low-2|SPX Low-3|SPX Low-4|SPX Low-5|SPX Low-6|SPX Low-7|SPX Low-8|SPX Low-9|SPX Open-0|SPX Open-1|SPX Open-2|SPX Open-3|SPX Open-4|SPX Open-5|SPX Open-6|SPX Open-7|SPX Open-8|SPX Open-9"/>
</operator>
<operator activated="false" class="h2o:gradient_boosted_trees" compatibility="7.4.000" expanded="true" height="103" name="Gradient Boosted Trees" width="90" x="782" y="34">
<list key="expert_parameters"/>
</operator>
<operator activated="false" class="select_attributes" compatibility="7.4.000" expanded="true" height="82" name="Select Attributes (2)" width="90" x="313" y="289">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attributes" value="SPX Close-0|SPX Close-1|SPX Close-2|SPX Close-3|SPX Close-4|SPX Close-5|SPX Close-6|SPX Close-7|SPX Close-8|SPX Close-9|SPX High-0|SPX High-1|SPX High-2|SPX High-3|SPX High-4|SPX High-5|SPX High-6|SPX High-7|SPX High-8|SPX High-9|SPX Low-0|SPX Low-1|SPX Low-2|SPX Low-3|SPX Low-4|SPX Low-5|SPX Low-6|SPX Low-7|SPX Low-8|SPX Low-9|SPX Open-0|SPX Open-1|SPX Open-2|SPX Open-3|SPX Open-4|SPX Open-5|SPX Open-6|SPX Open-7|SPX Open-8|SPX Open-9|SPX-9|SPX-8|SPX-7|SPX-6|SPX-5|SPX-4|SPX-3|SPX-2|SPX-1|SPX-0|SPX Open-99|SPX Open-98|SPX Open-97|SPX Open-96|SPX Open-95|SPX Open-94|SPX Open-93|SPX Open-92|SPX Open-91|SPX Open-90|SPX Open-89|SPX Open-88|SPX Open-87|SPX Open-86|SPX Open-85|SPX Open-84|SPX Open-83|SPX Open-82|SPX Open-81|SPX Open-80|SPX Open-79|SPX Open-78|SPX Open-77|SPX Open-76|SPX Open-75|SPX Open-74|SPX Open-73|SPX Open-72|SPX Open-71|SPX Open-70|SPX Open-69|SPX Open-68|SPX Open-67|SPX Open-66|SPX Open-65|SPX Open-64|SPX Open-63|SPX Open-62|SPX Open-61|SPX Open-60|SPX Open-59|SPX Open-58|SPX Open-57|SPX Open-56|SPX Open-55|SPX Open-54|SPX Open-53|SPX Open-52|SPX Open-51|SPX Open-50|SPX Open-49|SPX Open-48|SPX Open-47|SPX Open-46|SPX Open-45|SPX Open-44|SPX Open-43|SPX Open-42|SPX Open-41|SPX Open-40|SPX Open-39|SPX Open-38|SPX Open-37|SPX Open-36|SPX Open-35|SPX Open-34|SPX Open-33|SPX Open-32|SPX Open-31|SPX Open-30|SPX Open-29|SPX Open-28|SPX Open-27|SPX Open-26|SPX Open-25|SPX Open-24|SPX Open-23|SPX Open-22|SPX Open-21|SPX Open-20|SPX Open-19|SPX Open-18|SPX Open-17|SPX Open-16|SPX Open-15|SPX Open-14|SPX Open-13|SPX Open-12|SPX Open-11|SPX Open-10|SPX Low-99|SPX Low-98|SPX Low-97|SPX Low-96|SPX Low-95|SPX Low-94|SPX Low-93|SPX Low-92|SPX Low-91|SPX Low-90|SPX Low-89|SPX Low-88|SPX Low-87|SPX Low-86|SPX Low-85|SPX Low-84|SPX Low-83|SPX Low-82|SPX Low-81|SPX Low-80|SPX Low-79|SPX Low-78|SPX Low-77|SPX Low-76|SPX Low-75|SPX Low-74|SPX Low-73|SPX Low-72|SPX Low-71|SPX Low-70|SPX Low-69|SPX Low-68|SPX Low-67|SPX Low-66|SPX Low-65|SPX Low-64|SPX Low-63|SPX Low-62|SPX Low-61|SPX Low-60|SPX Low-59|SPX Low-58|SPX Low-57|SPX Low-56|SPX Low-55|SPX Low-54|SPX Low-53|SPX Low-52|SPX Low-51|SPX Low-50|SPX Low-49|SPX Low-48|SPX Low-47|SPX Low-46|SPX Low-45|SPX Low-44|SPX Low-43|SPX Low-42|SPX Low-41|SPX Low-40|SPX Low-39|SPX Low-38|SPX Low-37|SPX Low-36|SPX Low-35|SPX Low-34|SPX Low-33|SPX Low-32|SPX Low-31|SPX Low-30|SPX Low-29|SPX Low-28|SPX Low-27|SPX Low-26|SPX Low-25|SPX Low-24|SPX Low-23|SPX Low-22|SPX Low-21|SPX Low-20|SPX Low-19|SPX Low-18|SPX Low-17|SPX Low-16|SPX Low-15|SPX Low-14|SPX Low-13|SPX Low-12|SPX Low-11|SPX Low-10|SPX High-99|SPX High-98|SPX High-97|SPX High-96|SPX High-95|SPX High-94|SPX High-93|SPX High-92|SPX High-91|SPX High-90|SPX High-89|SPX High-88|SPX High-87|SPX High-86|SPX High-85|SPX High-84|SPX High-83|SPX High-82|SPX High-81|SPX High-80|SPX High-79|SPX High-78|SPX High-77|SPX High-76|SPX High-75|SPX High-74|SPX High-73|SPX High-72|SPX High-71|SPX High-70|SPX High-69|SPX High-68|SPX High-67|SPX High-66|SPX High-65|SPX High-64|SPX High-63|SPX High-62|SPX High-61|SPX High-60|SPX High-59|SPX High-58|SPX High-57|SPX High-56|SPX High-55|SPX High-54|SPX High-53|SPX High-52|SPX High-51|SPX High-50|SPX High-49|SPX High-48|SPX High-47|SPX High-46|SPX High-45|SPX High-44|SPX High-43|SPX High-42|SPX High-41|SPX High-40|SPX High-39|SPX High-38|SPX High-37|SPX High-36|SPX High-35|SPX High-34|SPX High-33|SPX High-32|SPX High-31|SPX High-30|SPX High-29|SPX High-28|SPX High-27|SPX High-26|SPX High-25|SPX High-24|SPX High-23|SPX High-22|SPX High-21|SPX High-20|SPX High-19|SPX High-18|SPX High-17|SPX High-16|SPX High-15|SPX High-14|SPX High-13|SPX High-12|SPX High-11|SPX High-10|SPX Close-99|SPX Close-98|SPX Close-97|SPX Close-96|SPX Close-95|SPX Close-94|SPX Close-93|SPX Close-92|SPX Close-91|SPX Close-90|SPX Close-89|SPX Close-88|SPX Close-87|SPX Close-86|SPX Close-85|SPX Close-84|SPX Close-83|SPX Close-82|SPX Close-81|SPX Close-80|SPX Close-79|SPX Close-78|SPX Close-77|SPX Close-76|SPX Close-75|SPX Close-74|SPX Close-73|SPX Close-72|SPX Close-71|SPX Close-70|SPX Close-69|SPX Close-68|SPX Close-67|SPX Close-66|SPX Close-65|SPX Close-64|SPX Close-63|SPX Close-62|SPX Close-61|SPX Close-60|SPX Close-59|SPX Close-58|SPX Close-57|SPX Close-56|SPX Close-55|SPX Close-54|SPX Close-53|SPX Close-52|SPX Close-51|SPX Close-50|SPX Close-49|SPX Close-48|SPX Close-47|SPX Close-46|SPX Close-45|SPX Close-44|SPX Close-43|SPX Close-42|SPX Close-41|SPX Close-40|SPX Close-39|SPX Close-38|SPX Close-37|SPX Close-36|SPX Close-35|SPX Close-34|SPX Close-33|SPX Close-32|SPX Close-31|SPX Close-30|SPX Close-29|SPX Close-28|SPX Close-27|SPX Close-26|SPX Close-25|SPX Close-24|SPX Close-23|SPX Close-22|SPX Close-21|SPX Close-20|SPX Close-19|SPX Close-18|SPX Close-17|SPX Close-16|SPX Close-15|SPX Close-14|SPX Close-13|SPX Close-12|SPX Close-11|SPX Close-10"/>
</operator>
<operator activated="true" class="replace_missing_values" compatibility="7.4.000" expanded="true" height="103" name="Replace Missing Values" width="90" x="447" y="187">
<list key="columns"/>
</operator>
<operator activated="true" class="series:sliding_window_validation" compatibility="7.4.000" expanded="true" height="145" name="Validation (3)" width="90" x="581" y="187">
<parameter key="cumulative_training" value="true"/>
<process expanded="true">
<operator activated="false" class="concurrencySmiley Tonguearallel_decision_tree" compatibility="7.4.000" expanded="true" height="82" name="Decision Tree (5)" width="90" x="179" y="34"/>
<operator activated="true" class="h2o:gradient_boosted_trees" compatibility="7.4.000" expanded="true" height="103" name="Gradient Boosted Trees (6)" width="90" x="179" y="136">
<list key="expert_parameters"/>
</operator>
<connect from_port="training" to_op="Gradient Boosted Trees (6)" to_port="training set"/>
<connect from_op="Gradient Boosted Trees (6)" 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"/>
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<process expanded="true">
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<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_classification" compatibility="7.4.000" expanded="true" height="82" name="Performance (4)" width="90" x="246" y="34">
<list key="class_weights"/>
</operator>
<connect from_port="model" to_op="Apply Model (4)" to_port="model"/>
<connect from_port="test set" to_op="Apply Model (4)" to_port="unlabelled data"/>
<connect from_op="Apply Model (4)" from_port="labelled data" to_op="Performance (4)" to_port="labelled data"/>
<connect from_op="Performance (4)" 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"/>
<portSpacing port="sink_averagable 3" spacing="0"/>
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</operator>
<operator activated="false" class="split_validation" compatibility="7.4.000" expanded="true" height="124" name="Validation (2)" width="90" x="313" y="493">
<parameter key="split" value="absolute"/>
<parameter key="training_set_size" value="1000"/>
<parameter key="sampling_type" value="linear sampling"/>
<process expanded="true">
<operator activated="false" class="k_nn" compatibility="7.4.000" expanded="true" height="82" name="k-NN" width="90" x="112" y="187"/>
<operator activated="false" class="h2o:gradient_boosted_trees" compatibility="7.4.000" expanded="true" height="103" name="Gradient Boosted Trees (2)" width="90" x="112" y="34">
<parameter key="maximal_depth" value="10"/>
<list key="expert_parameters"/>
</operator>
<operator activated="false" class="concurrencySmiley Tonguearallel_decision_tree" compatibility="7.4.000" expanded="true" height="82" name="Decision Tree (2)" width="90" x="112" y="289"/>
<operator activated="false" class="bayesian_boosting" compatibility="7.4.000" expanded="true" height="82" name="Bayesian Boosting" width="90" x="112" y="493">
<process expanded="true">
<operator activated="true" class="h2o:gradient_boosted_trees" compatibility="7.4.000" expanded="true" height="103" name="Gradient Boosted Trees (3)" width="90" x="179" y="34">
<parameter key="number_of_trees" value="200"/>
<list key="expert_parameters"/>
</operator>
<connect from_port="training set" to_op="Gradient Boosted Trees (3)" to_port="training set"/>
<connect from_op="Gradient Boosted Trees (3)" from_port="model" to_port="model"/>
<portSpacing port="source_training set" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
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</operator>
<operator activated="false" class="weka:W-IBk" compatibility="7.3.000" expanded="true" height="82" name="W-IBk" width="90" x="246" y="34"/>
<operator activated="false" class="weka:W-KStar" compatibility="7.3.000" expanded="true" height="82" name="W-KStar" width="90" x="246" y="544"/>
<operator activated="false" class="weka:W-LWL" compatibility="7.3.000" expanded="true" height="82" name="W-LWL" width="90" x="246" y="238"/>
<operator activated="false" class="weka:W-LADTree" compatibility="7.3.000" expanded="true" height="82" name="W-LADTree" width="90" x="246" y="340"/>
<operator activated="false" class="weka:W-FT" compatibility="7.3.000" expanded="true" height="82" name="W-FT" width="90" x="246" y="442"/>
<operator activated="false" class="weka:W-NBTree" compatibility="7.3.000" expanded="true" height="82" name="W-NBTree" width="90" x="246" y="136"/>
<operator activated="false" class="support_vector_machine_evolutionary" compatibility="7.4.000" expanded="true" height="82" name="SVM" width="90" x="380" y="187"/>
<operator activated="false" class="bagging" compatibility="7.4.000" expanded="true" height="82" name="Bagging (2)" width="90" x="112" y="595">
<process expanded="true">
<operator activated="true" class="h2o:gradient_boosted_trees" compatibility="7.4.000" expanded="true" height="103" name="Gradient Boosted Trees (4)" width="90" x="179" y="34">
<list key="expert_parameters"/>
</operator>
<connect from_port="training set" to_op="Gradient Boosted Trees (4)" to_port="training set"/>
<connect from_op="Gradient Boosted Trees (4)" from_port="model" to_port="model"/>
<portSpacing port="source_training set" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
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</operator>
<operator activated="false" class="adaboost" compatibility="7.4.000" expanded="true" height="82" name="AdaBoost" width="90" x="112" y="391">
<process expanded="true">
<operator activated="false" class="concurrencySmiley Tonguearallel_decision_tree" compatibility="7.4.000" expanded="true" height="82" name="Decision Tree (3)" width="90" x="313" y="85"/>
<operator activated="false" class="h2o:gradient_boosted_trees" compatibility="7.4.000" expanded="true" height="103" name="Gradient Boosted Trees (5)" width="90" x="313" y="238">
<list key="expert_parameters"/>
</operator>
<operator activated="false" class="naive_bayes" compatibility="7.4.000" expanded="true" height="82" name="Naive Bayes" width="90" x="313" y="391"/>
<operator activated="true" class="h2o:logistic_regression" compatibility="7.4.000" expanded="true" height="103" name="Logistic Regression" width="90" x="313" y="493"/>
<connect from_port="training set" to_op="Logistic Regression" to_port="training set"/>
<connect from_op="Logistic Regression" from_port="model" to_port="model"/>
<portSpacing port="source_training set" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
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</operator>
<operator activated="false" class="h2o:logistic_regression" compatibility="7.4.000" expanded="true" height="103" name="Logistic Regression (2)" width="90" x="380" y="289"/>
<operator activated="true" class="linear_regression" compatibility="7.4.000" expanded="true" height="103" name="Linear Regression (2)" width="90" x="380" y="34"/>
<connect from_port="training" to_op="Linear Regression (2)" to_port="training set"/>
<connect from_op="Linear Regression (2)" from_port="model" to_port="model"/>
<connect from_op="Linear Regression (2)" from_port="exampleSet" to_port="through 1"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
<portSpacing port="sink_through 2" spacing="0"/>
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<process expanded="true">
<operator activated="true" class="apply_model" compatibility="7.4.000" expanded="true" height="82" name="Apply Model (2)" width="90" x="112" y="34">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_binominal_classification" compatibility="7.4.000" expanded="true" height="82" name="Performance (3)" width="90" x="246" y="34">
<parameter key="classification_error" value="true"/>
<parameter key="kappa" value="true"/>
<parameter key="AUC (optimistic)" value="true"/>
<parameter key="AUC" value="true"/>
<parameter key="AUC (pessimistic)" value="true"/>
<parameter key="precision" value="true"/>
<parameter key="recall" value="true"/>
<parameter key="false_positive" value="true"/>
<parameter key="false_negative" value="true"/>
<parameter key="true_positive" value="true"/>
<parameter key="true_negative" value="true"/>
<parameter key="sensitivity" value="true"/>
<parameter key="positive_predictive_value" value="true"/>
<parameter key="negative_predictive_value" value="true"/>
<parameter key="psep" value="true"/>
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<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 (3)" to_port="labelled data"/>
<connect from_op="Performance (3)" 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="source_through 2" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
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</operator>
<operator activated="false" class="linear_regression" compatibility="7.4.000" expanded="true" height="103" name="Linear Regression" width="90" x="782" y="238">
<parameter key="feature_selection" value="greedy"/>
<parameter key="eliminate_colinear_features" value="false"/>
</operator>
<operator activated="false" class="apply_model" compatibility="7.4.000" expanded="true" height="82" name="Apply Model" width="90" x="782" y="136">
<list key="application_parameters"/>
</operator>
<operator activated="false" class="performance_binominal_classification" compatibility="7.4.000" expanded="true" height="82" name="Performance (2)" width="90" x="916" y="136">
<parameter key="classification_error" value="true"/>
<parameter key="precision" value="true"/>
<parameter key="false_positive" value="true"/>
<parameter key="false_negative" value="true"/>
<parameter key="true_positive" value="true"/>
<parameter key="true_negative" value="true"/>
<parameter key="positive_predictive_value" value="true"/>
<parameter key="negative_predictive_value" value="true"/>
</operator>
<operator activated="false" class="concurrency:cross_validation" compatibility="7.4.000" expanded="true" height="145" name="Cross Validation" width="90" x="179" y="493">
<parameter key="sampling_type" value="linear sampling"/>
<process expanded="true">
<operator activated="true" class="concurrencySmiley Tonguearallel_decision_tree" compatibility="7.4.000" expanded="true" height="82" name="Decision Tree (4)" width="90" x="112" y="34"/>
<connect from_port="training set" to_op="Decision Tree (4)" to_port="training set"/>
<connect from_op="Decision Tree (4)" 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="7.4.000" expanded="true" height="82" name="Apply Model (3)" width="90" x="112" y="34">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_classification" compatibility="7.4.000" expanded="true" height="82" name="Performance" width="90" x="246" y="34">
<list key="class_weights"/>
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<connect from_port="model" to_op="Apply Model (3)" to_port="model"/>
<connect from_port="test set" to_op="Apply Model (3)" to_port="unlabelled data"/>
<connect from_op="Apply Model (3)" 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>
<connect from_op="Retrieve Date_NDX_SPX_VIX_RUT_DJX_HOLC Data (2)" 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="Windowing" to_port="example set input"/>
<connect from_op="Windowing" from_port="example set output" to_op="Generate Attributes" to_port="example set input"/>
<connect from_op="Generate Attributes" from_port="example set output" to_op="Set Role (2)" to_port="example set input"/>
<connect from_op="Set Role (2)" from_port="example set output" to_op="Differencing" to_port="example set input"/>
<connect from_op="Differencing" from_port="example set output" to_op="Replace Missing Values" to_port="example set input"/>
<connect from_op="Replace Missing Values" from_port="example set output" to_op="Validation (3)" to_port="training"/>
<connect from_op="Validation (3)" from_port="model" to_port="result 1"/>
<connect from_op="Validation (3)" from_port="training" to_port="result 2"/>
<connect from_op="Validation (3)" from_port="averagable 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"/>
</process>
</operator>
</process>

RM Certified Expert
RM Certified Expert
Solution

Re: Making Label an Attribute - Decision Tree Process

You're going to want to use a Sliding Window Validation operator if you're testing time series.

 

You might want to check out my updated tutorial here: http://www.neuralmarkettrends.com/building-an-ai-financial-market-model-lesson-iv/

 

Learner III Eric005
Learner III

Re: Making Label an Attribute - Decision Tree Process

Thanks Thomas

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