Visibility: public Uploaded 19-04-2018 by Tim Beurskens RapidMiner_8.1.001 1 runs
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LogisticRegressionSVM__CThe SVM complexity constant. Use -1 for different C values for positive and negative.default: 1.0
LogisticRegressionSVM__calculate_weightsIndicates if attribute weights should be returned.default: true
LogisticRegressionSVM__convergence_epsilonPrecision on the KKT conditionsdefault: 0.001
LogisticRegressionSVM__kernel_aThe SVM kernel parameter a.default: 1.0
LogisticRegressionSVM__kernel_bThe SVM kernel parameter b.default: 0.0
LogisticRegressionSVM__kernel_cacheSize of the cache for kernel evaluations im MBdefault: 200
LogisticRegressionSVM__kernel_degreeThe SVM kernel parameter degree.default: 2.0
LogisticRegressionSVM__kernel_gammaThe SVM kernel parameter gamma.default: 1.0
LogisticRegressionSVM__kernel_shiftThe SVM kernel parameter shift.default: 1.0
LogisticRegressionSVM__kernel_sigma1The SVM kernel parameter sigma1.default: 1.0
LogisticRegressionSVM__kernel_sigma2The SVM kernel parameter sigma2.default: 0.0
LogisticRegressionSVM__kernel_sigma3The SVM kernel parameter sigma3.default: 2.0
LogisticRegressionSVM__kernel_typeThe SVM kernel typedefault: dot
LogisticRegressionSVM__max_iterationsStop after this many iterationsdefault: 100000
LogisticRegressionSVM__return_optimization_performanceIndicates if final optimization fitness should be returned as performance.default: true
LogisticRegressionSVM__scaleScale the example values and store the scaling parameters for test set.default: true
OptimizeSelection__constraint_draw_rangeDetermines if the draw range of the population plotter should be constrained between 0 and 1.default: false
OptimizeSelection__draw_dominated_pointsDetermines if only points which are not Pareto dominated should be painted.default: true
OptimizeSelection__generations_without_improvalStop after n generations without improval of the performance.default: 1
OptimizeSelection__keep_bestKeep the best n individuals in each generation.default: 1
OptimizeSelection__limit_generations_without_improvalIndicates if the optimization should be aborted if this number of generations showed no improvement. If unchecked, always the maximal number of generations will be used.default: true
OptimizeSelection__limit_number_of_generationsDefines if the number of generations should be limited on a specific number.default: false
OptimizeSelection__local_random_seedSpecifies the local random seeddefault: 1992
OptimizeSelection__maximal_fitnessThe optimization will stop if the fitness reaches the defined maximum.default: Infinity
OptimizeSelection__maximum_number_of_generationsDefines the maximum amount of generations.default: 10
OptimizeSelection__normalize_weightsIndicates if the final weights should be normalized.default: true
OptimizeSelection__plot_generationsUpdate the population plotter in these generations.default: 10
OptimizeSelection__population_criteria_data_fileThe path to the file in which the criteria data of the final population should be saved.
OptimizeSelection__selection_directionForward selection or backward elimination.default: forward
OptimizeSelection__show_population_plotterDetermines if the current population should be displayed in performance space.default: false
OptimizeSelection__use_local_random_seedIndicates if a local random seed should be used.default: false
OptimizeSelection__user_result_individual_selectionDetermines if the user wants to select the final result individual from the last population.default: false


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