| LogisticRegressionSVM__C | The SVM complexity constant. Use -1 for different C values for positive and negative. | default: 1.0 |
| LogisticRegressionSVM__calculate_weights | Indicates if attribute weights should be returned. | default: true |
| LogisticRegressionSVM__convergence_epsilon | Precision on the KKT conditions | default: 0.001 |
| LogisticRegressionSVM__kernel_a | The SVM kernel parameter a. | default: 1.0 |
| LogisticRegressionSVM__kernel_b | The SVM kernel parameter b. | default: 0.0 |
| LogisticRegressionSVM__kernel_cache | Size of the cache for kernel evaluations im MB | default: 200 |
| LogisticRegressionSVM__kernel_degree | The SVM kernel parameter degree. | default: 2.0 |
| LogisticRegressionSVM__kernel_gamma | The SVM kernel parameter gamma. | default: 1.0 |
| LogisticRegressionSVM__kernel_shift | The SVM kernel parameter shift. | default: 1.0 |
| LogisticRegressionSVM__kernel_sigma1 | The SVM kernel parameter sigma1. | default: 1.0 |
| LogisticRegressionSVM__kernel_sigma2 | The SVM kernel parameter sigma2. | default: 0.0 |
| LogisticRegressionSVM__kernel_sigma3 | The SVM kernel parameter sigma3. | default: 2.0 |
| LogisticRegressionSVM__kernel_type | The SVM kernel type | default: dot |
| LogisticRegressionSVM__max_iterations | Stop after this many iterations | default: 100000 |
| LogisticRegressionSVM__return_optimization_performance | Indicates if final optimization fitness should be returned as performance. | default: true |
| LogisticRegressionSVM__scale | Scale the example values and store the scaling parameters for test set. | default: true |
| OptimizeSelection__constraint_draw_range | Determines if the draw range of the population plotter should be constrained between 0 and 1. | default: false |
| OptimizeSelection__draw_dominated_points | Determines if only points which are not Pareto dominated should be painted. | default: true |
| OptimizeSelection__generations_without_improval | Stop after n generations without improval of the performance. | default: 1 |
| OptimizeSelection__keep_best | Keep the best n individuals in each generation. | default: 1 |
| OptimizeSelection__limit_generations_without_improval | Indicates 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_generations | Defines if the number of generations should be limited on a specific number. | default: false |
| OptimizeSelection__local_random_seed | Specifies the local random seed | default: 1992 |
| OptimizeSelection__maximal_fitness | The optimization will stop if the fitness reaches the defined maximum. | default: Infinity |
| OptimizeSelection__maximum_number_of_generations | Defines the maximum amount of generations. | default: 10 |
| OptimizeSelection__normalize_weights | Indicates if the final weights should be normalized. | default: true |
| OptimizeSelection__plot_generations | Update the population plotter in these generations. | default: 10 |
| OptimizeSelection__population_criteria_data_file | The path to the file in which the criteria data of the final population should be saved. | |
| OptimizeSelection__selection_direction | Forward selection or backward elimination. | default: forward |
| OptimizeSelection__show_population_plotter | Determines if the current population should be displayed in performance space. | default: false |
| OptimizeSelection__use_local_random_seed | Indicates if a local random seed should be used. | default: false |
| OptimizeSelection__user_result_individual_selection | Determines if the user wants to select the final result individual from the last population. | default: false |