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<operator name="Root" class="Process" expanded="yes"> <description text="This experiment is very similar to the previous experiment. Again, a cross validation building block is used as fitness evaluation. In this case, a genetic algortihm for feature selection is used. This experiment demonstrates two other features of the feature operators of RapidMiner: the stop button which allows the abort of the experiment if the user was already satisfied and the ProcessLog operator which allows online plotting of current fitness values. Please refer to the visualisation sample experiments and the RapidMiner operator reference for further details."/> <parameter key="random_seed" value="1976"/> <operator name="ExampleSetGenerator" class="ExampleSetGenerator"> <parameter key="attributes_lower_bound" value="0.0"/> <parameter key="number_examples" value="200"/> <parameter key="number_of_attributes" value="4"/> <parameter key="target_function" value="sum"/> </operator> <operator name="NoiseGenerator" class="NoiseGenerator"> <parameter key="label_noise" value="0.0010"/> <list key="noise"> </list> <parameter key="random_attributes" value="8"/> </operator> <operator name="GeneticAlgorithm" class="GeneticAlgorithm" expanded="yes"> <parameter key="maximum_number_of_generations" value="12"/> <parameter key="population_size" value="6"/> <parameter key="selection_scheme" value="Boltzmann"/> <parameter key="show_stop_dialog" value="true"/> <operator name="OperatorChain" class="OperatorChain" expanded="yes"> <operator name="XValidation" class="XValidation" expanded="yes"> <parameter key="sampling_type" value="shuffled sampling"/> <operator name="LinearRegression" class="LinearRegression"> <parameter key="feature_selection" value="none"/> </operator> <operator name="OperatorChain (2)" class="OperatorChain" expanded="yes"> <operator name="ModelApplier" class="ModelApplier"> <list key="application_parameters"> </list> </operator> <operator name="RegressionPerformance" class="RegressionPerformance"> <parameter key="main_criterion" value="root_relative_squared_error"/> <parameter key="root_mean_squared_error" value="true"/> <parameter key="root_relative_squared_error" value="true"/> </operator> </operator> </operator> <operator name="ProcessLog" class="ProcessLog"> <list key="log"> <parameter key="gen" value="operator.GeneticAlgorithm.value.generation"/> <parameter key="perf" value="operator.GeneticAlgorithm.value.performance"/> <parameter key="best" value="operator.GeneticAlgorithm.value.best"/> </list> </operator> </operator> </operator></operator>