Multiple Stacking
mustafa_mert_ce
New Altair Community Member
Hi;
What I would like to do is to have multiple stackings so here is the deal;
I have some base learners in my stacking. As a stacked learner I use a neural network. What I'd like to do is to use several seperate neural networks and have their outputs voted.
So Base Learners --> Different Neural Networks ---> Averaging ---> Output.
I tried adding a "Vote" operator in Stacking's Stacked Learner side, putting Neural Nets in it but I keep having Attribute Name collisions. How can I realise this scheme?
What I want to do is multiple stacking, like taking base learners, giving their output to different neural networks, and taking those neural network outputs, averaging them and taking as the result.
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Answers
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Hi,
sure. There are I think two ways to do it. A combination of Stacking and Vote or use three Stackings (maybe switch roles), join and use Generate Aggreagtion to average the results.
The vorting one is attached.
~Martin
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<parameter key="repository_entry" value="//Samples/data/Deals-Testset"/>
</operator>
<operator activated="true" class="vote" compatibility="7.2.003" expanded="true" height="68" name="Vote" width="90" x="313" y="34">
<process expanded="true">
<operator activated="true" class="stacking" compatibility="7.2.003" expanded="true" height="68" name="Stacking" width="90" x="112" y="34">
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<connect from_port="training set 1" to_op="Naive Bayes" to_port="training set"/>
<connect from_port="training set 2" to_op="Decision Tree" to_port="training set"/>
<connect from_op="Naive Bayes" from_port="model" to_port="base model 1"/>
<connect from_op="Decision Tree" from_port="model" to_port="base model 2"/>
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<connect from_port="training set 1" to_op="Naive Bayes (2)" to_port="training set"/>
<connect from_port="training set 2" to_op="Decision Tree (2)" to_port="training set"/>
<connect from_op="Naive Bayes (2)" from_port="model" to_port="base model 1"/>
<connect from_op="Decision Tree (2)" from_port="model" to_port="base model 2"/>
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<operator activated="true" class="naive_bayes" compatibility="7.2.003" expanded="true" height="82" name="Naive Bayes (3)" width="90" x="112" y="136"/>
<operator activated="true" class="parallel_decision_tree" compatibility="7.2.003" expanded="true" height="82" name="Decision Tree (3)" width="90" x="112" y="289"/>
<connect from_port="training set 1" to_op="Naive Bayes (3)" to_port="training set"/>
<connect from_port="training set 2" to_op="Decision Tree (3)" to_port="training set"/>
<connect from_op="Naive Bayes (3)" from_port="model" to_port="base model 1"/>
<connect from_op="Decision Tree (3)" from_port="model" to_port="base model 2"/>
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</enumeration>
<enumeration key="hidden_dropout_ratios"/>
<list key="expert_parameters"/>
<description align="center" color="transparent" colored="false" width="126">rect</description>
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<connect from_port="training set 2" to_op="Stacking (2)" to_port="training set"/>
<connect from_port="training set 3" to_op="Stacking (3)" to_port="training set"/>
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</operator>
<connect from_op="Retrieve Deals-Testset" from_port="output" to_op="Vote" to_port="training set"/>
<connect from_op="Vote" 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"/>
</process>
</operator>
</process>1 -
Thanks! Just a quick question, do I need to enclose this vote in a Cross-Validation to ensure doing cross validation on this entire ensemble (whole vote)?
What I want to do is to do cross validation on just the outputs of vote, not the inner stackings. I don't want to minimize the RMSE of base learners in stackings, I only want to minimize the RMSE for the vote as suggested in BelKor's Pragmatic Chaos.0 -
Thanks! Just a quick question, do I need to enclose this vote in a Cross-Validation to ensure doing cross validation on this entire ensemble (whole vote)?
What I want to do is to do cross validation on just the outputs of vote, not the inner stackings. I don't want to minimize the RMSE of base learners in stackings, I only want to minimize the RMSE for the vote as suggested in BelKor's Pragmatic Chaos.0 -
x-val is estimating a performance of the method to generate a model. Your method to generate a model includes Stacking and Voting, so everything needs to be in an X-Val.
~Martin
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Altair Employee