rm.operator.support_vector_machine
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Uploaded 02-02-2016 by
Jan van Rijn
RapidMiner_6.4.0
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Parameters
| C | The SVM complexity constant. Use -1 for different C values for positive and negative. | default: 0.0 |
| L_neg | A factor for the SVM complexity constant for negative examples | default: 1.0 |
| L_pos | A factor for the SVM complexity constant for positive examples | default: 1.0 |
| balance_cost | Adapts Cpos and Cneg to the relative size of the classes | default: false |
| calculate_weights | Indicates if attribute weights should be returned. | default: true |
| convergence_epsilon | Precision on the KKT conditions | default: 0.001 |
| epsilon | Insensitivity constant. No loss if prediction lies this close to true value | default: 0.0 |
| epsilon_minus | Epsilon for negative deviation only | default: 0.0 |
| epsilon_plus | Epsilon for positive deviation only | default: 0.0 |
| estimate_performance | Indicates if this learner should also return a performance estimation. | default: false |
| kernel_a | The SVM kernel parameter a. | default: 1.0 |
| kernel_b | The SVM kernel parameter b. | default: 0.0 |
| kernel_cache | Size of the cache for kernel evaluations im MB | default: 200 |
| kernel_degree | The SVM kernel parameter degree. | default: 2.0 |
| kernel_gamma | The SVM kernel parameter gamma. | default: 1.0 |
| kernel_shift | The SVM kernel parameter shift. | default: 1.0 |
| kernel_sigma1 | The SVM kernel parameter sigma1. | default: 1.0 |
| kernel_sigma2 | The SVM kernel parameter sigma2. | default: 0.0 |
| kernel_sigma3 | The SVM kernel parameter sigma3. | default: 2.0 |
| kernel_type | The SVM kernel type | default: dot |
| max_iterations | Stop after this many iterations | default: 100000 |
| quadratic_loss_neg | Use quadratic loss for negative deviation | default: false |
| quadratic_loss_pos | Use quadratic loss for positive deviation | default: false |
| return_optimization_performance | Indicates if final optimization fitness should be returned as performance. | default: true |
| scale | Scale the example values and store the scaling parameters for test set. | default: true |
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