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Hi,
You can use a command line version of the RapidMiner engine or install a deployment server on that device to use all models directly. I would definitely consider this if possible.
Most models do not support the generation of "easy" formulas. There is actually an operator for this extraction, but only few model types are supported. GBT is not one of them. Here is an example process nevertheless.
Cheers,
Ingo
Ingo
<?xml version="1.0" encoding="UTF-8"?><process version="9.6.000-SNAPSHOT"><br> <context><br> <input/><br> <output/><br> <macros/><br> </context><br> <operator activated="true" class="process" compatibility="9.6.000-SNAPSHOT" expanded="true" name="Process"><br> <parameter key="logverbosity" value="init"/><br> <parameter key="random_seed" value="2001"/><br> <parameter key="send_mail" value="never"/><br> <parameter key="notification_email" value=""/><br> <parameter key="process_duration_for_mail" value="30"/><br> <parameter key="encoding" value="UTF-8"/><br> <process expanded="true"><br> <operator activated="true" class="retrieve" compatibility="9.6.000-SNAPSHOT" expanded="true" height="68" name="Retrieve Sonar" width="90" x="45" y="34"><br> <parameter key="repository_entry" value="//Samples/data/Sonar"/><br> </operator><br> <operator activated="true" class="support_vector_machine" compatibility="9.6.000-SNAPSHOT" expanded="true" height="124" name="SVM" width="90" x="179" y="34"><br> <parameter key="kernel_type" value="dot"/><br> <parameter key="kernel_gamma" value="1.0"/><br> <parameter key="kernel_sigma1" value="1.0"/><br> <parameter key="kernel_sigma2" value="0.0"/><br> <parameter key="kernel_sigma3" value="2.0"/><br> <parameter key="kernel_shift" value="1.0"/><br> <parameter key="kernel_degree" value="2.0"/><br> <parameter key="kernel_a" value="1.0"/><br> <parameter key="kernel_b" value="0.0"/><br> <parameter key="kernel_cache" value="200"/><br> <parameter key="C" value="0.0"/><br> <parameter key="convergence_epsilon" value="0.001"/><br> <parameter key="max_iterations" value="100000"/><br> <parameter key="scale" value="true"/><br> <parameter key="calculate_weights" value="true"/><br> <parameter key="return_optimization_performance" value="true"/><br> <parameter key="L_pos" value="1.0"/><br> <parameter key="L_neg" value="1.0"/><br> <parameter key="epsilon" value="0.0"/><br> <parameter key="epsilon_plus" value="0.0"/><br> <parameter key="epsilon_minus" value="0.0"/><br> <parameter key="balance_cost" value="false"/><br> <parameter key="quadratic_loss_pos" value="false"/><br> <parameter key="quadratic_loss_neg" value="false"/><br> <parameter key="estimate_performance" value="false"/><br> </operator><br> <operator activated="true" class="create_formula" compatibility="9.6.000-SNAPSHOT" expanded="true" height="82" name="Create Formula" width="90" x="313" y="34"/><br> <connect from_op="Retrieve Sonar" from_port="output" to_op="SVM" to_port="training set"/><br> <connect from_op="SVM" from_port="model" to_op="Create Formula" to_port="model"/><br> <connect from_op="Create Formula" from_port="formula" to_port="result 1"/><br> <portSpacing port="source_input 1" spacing="0"/><br> <portSpacing port="sink_result 1" spacing="0"/><br> <portSpacing port="sink_result 2" spacing="0"/><br> </process><br> </operator><br></process><br><br>
Hi,
There have been a couple of examples here on the community for that, see for example:
There have been a couple of examples here on the community for that, see for example:
Hope this helps,
Ingo
Ingo
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Hello @klugman
Do you want to extract gradient boosted tree predictions and create a visualization?
If so you can extract predictions from the apply model operators. You can also store the trained model using the "Store" operator in rapidminer and retrieve this to apply on a new dataset for predictions in rapidminer. I don't think you can apply the trained model outside of rapidminer environment.
Gradient boosted trees are additive models and there won't be a single equation that can be extracted from the model.
Do you want to extract gradient boosted tree predictions and create a visualization?
If so you can extract predictions from the apply model operators. You can also store the trained model using the "Store" operator in rapidminer and retrieve this to apply on a new dataset for predictions in rapidminer. I don't think you can apply the trained model outside of rapidminer environment.
Gradient boosted trees are additive models and there won't be a single equation that can be extracted from the model.
Do you want to extract gradient boosted tree predictions and create a visualization?
If so you can extract predictions from the apply model operators. You can also store the trained model using the "Store" operator in rapidminer and retrieve this to apply on a new dataset for predictions in rapidminer. I don't think you can apply the trained model outside of rapidminer environment.
Gradient boosted trees are additive models and there won't be a single equation that can be extracted from the model.