| Bagging2__average_confidences | Specifies whether to average available prediction confidences or not. | default: true |
| Bagging2__iterations | The number of iterations (base models). | default: 10 |
| Bagging2__local_random_seed | Specifies the local random seed | default: 1992 |
| Bagging2__sample_ratio | Fraction of examples used for training. Must be greater than 0 and should be lower than 1. | default: 0.9 |
| Bagging2__use_local_random_seed | Indicates if a local random seed should be used. | default: false |
| Normalize__allow_negative_values | Whether negative values should be allowed and used as absolute values | default: false |
| Normalize__attribute | The attribute which should be chosen. | |
| Normalize__attribute_filter_type | The condition specifies which attributes are selected or affected by this operator. | default: all |
| Normalize__attributes | The attribute which should be chosen. | |
| Normalize__block_type | The block type of the attributes. | default: value_series |
| Normalize__create_view | Create View to apply preprocessing instead of changing the data | default: false |
| Normalize__except_block_type | Except this block type. | default: value_series_end |
| Normalize__except_regular_expression | A regular expression for the names of the attributes which should be filtered out although matching the above regular expression. | |
| Normalize__except_value_type | Except this value type. | default: real |
| Normalize__include_special_attributes | Indicate if this operator should also be applied on the special attributes. Otherwise they are always kept. | default: false |
| Normalize__invert_selection | Indicates if only attributes should be accepted which would normally filtered. | default: false |
| Normalize__max | The maximum value after normalization | default: 1.0 |
| Normalize__method | Select the normalization method. | default: Z-transformation |
| Normalize__min | The minimum value after normalization | default: 0.0 |
| Normalize__numeric_condition | Parameter string for the condition, e.g. '>= 5' | |
| Normalize__regular_expression | A regular expression for the names of the attributes which should be kept. | |
| Normalize__return_preprocessing_model | Indicates if the preprocessing model should also be returned | default: false |
| Normalize__use_block_type_exception | If enabled, an exception to the specified block type might be specified. | default: false |
| Normalize__use_except_expression | If enabled, an exception to the specified regular expression might be specified. Attributes of matching this will be filtered out, although matching the first expression. | default: false |
| Normalize__use_value_type_exception | If enabled, an exception to the specified value type might be specified. Attributes of this type will be filtered out, although matching the first specified type. | default: false |
| Normalize__value_type | The value type of the attributes. | default: numeric |
| PCA3__dimensionality_reduction | Indicates which type of dimensionality reduction should be applied | default: keep variance |
| PCA3__number_of_components | Keep this number of components. | default: 1 |
| PCA3__variance_threshold | Keep the all components with a cumulative variance smaller than the given threshold. | default: 0.95 |
| SVM2__C | The SVM complexity constant. Use -1 for different C values for positive and negative. | default: 0.0 |
| SVM2__L_neg | A factor for the SVM complexity constant for negative examples | default: 1.0 |
| SVM2__L_pos | A factor for the SVM complexity constant for positive examples | default: 1.0 |
| SVM2__balance_cost | Adapts Cpos and Cneg to the relative size of the classes | default: false |
| SVM2__calculate_weights | Indicates if attribute weights should be returned. | default: true |
| SVM2__convergence_epsilon | Precision on the KKT conditions | default: 0.001 |
| SVM2__epsilon | Insensitivity constant. No loss if prediction lies this close to true value | default: 0.0 |
| SVM2__epsilon_minus | Epsilon for negative deviation only | default: 0.0 |
| SVM2__epsilon_plus | Epsilon for positive deviation only | default: 0.0 |
| SVM2__estimate_performance | Indicates if this learner should also return a performance estimation. | default: false |
| SVM2__kernel_a | The SVM kernel parameter a. | default: 1.0 |
| SVM2__kernel_b | The SVM kernel parameter b. | default: 0.0 |
| SVM2__kernel_cache | Size of the cache for kernel evaluations im MB | default: 200 |
| SVM2__kernel_degree | The SVM kernel parameter degree. | default: 2.0 |
| SVM2__kernel_gamma | The SVM kernel parameter gamma. | default: 1.0 |
| SVM2__kernel_shift | The SVM kernel parameter shift. | default: 1.0 |
| SVM2__kernel_sigma1 | The SVM kernel parameter sigma1. | default: 1.0 |
| SVM2__kernel_sigma2 | The SVM kernel parameter sigma2. | default: 0.0 |
| SVM2__kernel_sigma3 | The SVM kernel parameter sigma3. | default: 2.0 |
| SVM2__kernel_type | The SVM kernel type | default: dot |
| SVM2__max_iterations | Stop after this many iterations | default: 100000 |
| SVM2__quadratic_loss_neg | Use quadratic loss for negative deviation | default: false |
| SVM2__quadratic_loss_pos | Use quadratic loss for positive deviation | default: false |
| SVM2__return_optimization_performance | Indicates if final optimization fitness should be returned as performance. | default: true |
| SVM2__scale | Scale the example values and store the scaling parameters for test set. | default: true |