Visibility: public Uploaded 02-02-2016 by Jan van Rijn RapidMiner_6.4.0 0 runs
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A RapidMiner Operator


CThe SVM complexity constant. Use -1 for different C values for positive and negative.default: 0.0
L_negA factor for the SVM complexity constant for negative examplesdefault: 1.0
L_posA factor for the SVM complexity constant for positive examplesdefault: 1.0
balance_costAdapts Cpos and Cneg to the relative size of the classesdefault: false
calculate_weightsIndicates if attribute weights should be returned.default: true
convergence_epsilonPrecision on the KKT conditionsdefault: 0.001
epsilonInsensitivity constant. No loss if prediction lies this close to true valuedefault: 0.0
epsilon_minusEpsilon for negative deviation onlydefault: 0.0
epsilon_plusEpsilon for positive deviation onlydefault: 0.0
estimate_performanceIndicates if this learner should also return a performance estimation.default: false
kernel_aThe SVM kernel parameter a.default: 1.0
kernel_bThe SVM kernel parameter b.default: 0.0
kernel_cacheSize of the cache for kernel evaluations im MBdefault: 200
kernel_degreeThe SVM kernel parameter degree.default: 2.0
kernel_gammaThe SVM kernel parameter gamma.default: 1.0
kernel_shiftThe SVM kernel parameter shift.default: 1.0
kernel_sigma1The SVM kernel parameter sigma1.default: 1.0
kernel_sigma2The SVM kernel parameter sigma2.default: 0.0
kernel_sigma3The SVM kernel parameter sigma3.default: 2.0
kernel_typeThe SVM kernel typedefault: dot
max_iterationsStop after this many iterationsdefault: 100000
quadratic_loss_negUse quadratic loss for negative deviationdefault: false
quadratic_loss_posUse quadratic loss for positive deviationdefault: false
return_optimization_performanceIndicates if final optimization fitness should be returned as performance.default: true
scaleScale the example values and store the scaling parameters for test set.default: true


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