Flow
rm.process(normalize,support_vector_machine,store)

rm.process(normalize,support_vector_machine,store)

Visibility: public Uploaded 17-04-2018 by Luud Janssen RapidMiner_8.1.001 1 runs
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
A RapidMiner Flow

Parameters

Normalize__allow_negative_valuesWhether negative values should be allowed and used as absolute valuesdefault: false
Normalize__attributeThe attribute which should be chosen.
Normalize__attribute_filter_typeThe condition specifies which attributes are selected or affected by this operator.default: all
Normalize__attributesThe attribute which should be chosen.
Normalize__block_typeThe block type of the attributes.default: value_series
Normalize__create_viewCreate View to apply preprocessing instead of changing the datadefault: false
Normalize__except_block_typeExcept this block type.default: value_series_end
Normalize__except_regular_expressionA regular expression for the names of the attributes which should be filtered out although matching the above regular expression.
Normalize__except_value_typeExcept this value type.default: real
Normalize__include_special_attributesIndicate if this operator should also be applied on the special attributes. Otherwise they are always kept.default: false
Normalize__invert_selectionIndicates if only attributes should be accepted which would normally filtered.default: false
Normalize__maxThe maximum value after normalizationdefault: 1.0
Normalize__methodSelect the normalization method.default: Z-transformation
Normalize__minThe minimum value after normalizationdefault: 0.0
Normalize__numeric_conditionParameter string for the condition, e.g. '>= 5'
Normalize__regular_expressionA regular expression for the names of the attributes which should be kept.
Normalize__return_preprocessing_modelIndicates if the preprocessing model should also be returneddefault: false
Normalize__use_block_type_exceptionIf enabled, an exception to the specified block type might be specified.default: false
Normalize__use_except_expressionIf 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_exceptionIf 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_typeThe value type of the attributes.default: numeric
SVM__CThe SVM complexity constant. Use -1 for different C values for positive and negative.default: 0.0
SVM__L_negA factor for the SVM complexity constant for negative examplesdefault: 1.0
SVM__L_posA factor for the SVM complexity constant for positive examplesdefault: 1.0
SVM__balance_costAdapts Cpos and Cneg to the relative size of the classesdefault: false
SVM__calculate_weightsIndicates if attribute weights should be returned.default: true
SVM__convergence_epsilonPrecision on the KKT conditionsdefault: 0.001
SVM__epsilonInsensitivity constant. No loss if prediction lies this close to true valuedefault: 0.0
SVM__epsilon_minusEpsilon for negative deviation onlydefault: 0.0
SVM__epsilon_plusEpsilon for positive deviation onlydefault: 0.0
SVM__estimate_performanceIndicates if this learner should also return a performance estimation.default: false
SVM__kernel_aThe SVM kernel parameter a.default: 1.0
SVM__kernel_bThe SVM kernel parameter b.default: 0.0
SVM__kernel_cacheSize of the cache for kernel evaluations im MBdefault: 200
SVM__kernel_degreeThe SVM kernel parameter degree.default: 2.0
SVM__kernel_gammaThe SVM kernel parameter gamma.default: 1.0
SVM__kernel_shiftThe SVM kernel parameter shift.default: 1.0
SVM__kernel_sigma1The SVM kernel parameter sigma1.default: 1.0
SVM__kernel_sigma2The SVM kernel parameter sigma2.default: 0.0
SVM__kernel_sigma3The SVM kernel parameter sigma3.default: 2.0
SVM__kernel_typeThe SVM kernel typedefault: dot
SVM__max_iterationsStop after this many iterationsdefault: 100000
SVM__quadratic_loss_negUse quadratic loss for negative deviationdefault: false
SVM__quadratic_loss_posUse quadratic loss for positive deviationdefault: false
SVM__return_optimization_performanceIndicates if final optimization fitness should be returned as performance.default: true
SVM__scaleScale the example values and store the scaling parameters for test set.default: true
Store2__repository_entryRepository entry.

0
Runs

List all runs
Parameter:
Rendering chart
Rendering table