| 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 |