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07_​Cross_​Validation_​with_​SVM_​and_​Parameter_​Optimization

Cross Validation with SVM and Parameter Optimization

A Cross-Validation setup is provided by using a Support-Vector-Machine (SVM) as base learning algorithm.

Cross Validation with SVM and Parameter Optimisation A simple example for the demonstration of Cross Validation combined with Parameter Optimization.The data is split into 5 subsets. The prediction of each subset is done by using an SVM model builton the other 4. The sigma parameter for the SVM Learner is varied and controlled by a flow variable.The Parameter Optimisation Loop Start node varies the value of the flow variable for each iteration.The Parameter Optimization Loop End node collects the results and output the value for theoptimized parameter yelding maximum accuracy as an objective function. cross validation:collect resultscross validation:partition datascore resultsread datause SVM topredict classesbuild SVMparameters to optimizecollect results X-Aggregator X-Partitioner Scorer Table Reader SVM Predictor SVM Learner Parameter OptimizationLoop Start ParameterOptimization Loop End Cross Validation with SVM and Parameter Optimisation A simple example for the demonstration of Cross Validation combined with Parameter Optimization.The data is split into 5 subsets. The prediction of each subset is done by using an SVM model builton the other 4. The sigma parameter for the SVM Learner is varied and controlled by a flow variable.The Parameter Optimisation Loop Start node varies the value of the flow variable for each iteration.The Parameter Optimization Loop End node collects the results and output the value for theoptimized parameter yelding maximum accuracy as an objective function. cross validation:collect resultscross validation:partition datascore resultsread datause SVM topredict classesbuild SVMparameters to optimizecollect resultsX-Aggregator X-Partitioner Scorer Table Reader SVM Predictor SVM Learner Parameter OptimizationLoop Start ParameterOptimization Loop End

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