Icon

WBCD

With SVM With K Nearest NeighborFirst try to find the value of k from k =1to 25 for the highest accuracy. With K Nearest NeighborRun a 10-fold CV with the value of kfound from the previous step, using allrows in the dataset. Breast Mass Classificationwith Support Vector Machineand k-Nearest Neighbor Pass the value of k as a variable. Read the Wiconsin BreastCancer DiagnosticCSV file Remove theID columnStratifiedpartitioningStratifiedNode 5Use validationdatasetNode 7Serialize the modelRead the modelUse test datasetFinal scoreNode 12Generate thevalues for kNode 14Node 15Node 16Pull Accuracy columnGet theOverall AccuracyvalueTable with the values of k andaccuracyAdd kto the table10-fold CVCreate and validatethe modelFinal scorePull Accuracy columnGet theOverall AccuracyvalueNode 26Node 27Pull Accuracy columnGet theOverall AccuracyvaluePlot k vs accuracyPull Accuracy columnGet theOverall AccuracyvalueNode 38Sort by Accuracy and kGet the first rowAdd method nameAdd methodnameAdd method nameUnionSUMMARYCSV Reader Column Filter Partitioning X-Partitioner SVM Learner SVM Predictor Scorer PMML Writer PMML Reader SVM Predictor Scorer Normalizer Table Creator Table Row ToVariable Loop Start K Nearest Neighbor Scorer Column Filter Row Filter Loop End Variable toTable Column X-Partitioner K Nearest Neighbor Scorer Column Filter Row Filter X-Aggregator X-Aggregator Column Filter Row Filter Line Plot(JavaScript) Column Filter Row Filter Table Rowto Variable Sorter Row Filter String Manipulation String Manipulation String Manipulation Concatenate(Optional in) Column Resorter With SVM With K Nearest NeighborFirst try to find the value of k from k =1to 25 for the highest accuracy. With K Nearest NeighborRun a 10-fold CV with the value of kfound from the previous step, using allrows in the dataset. Breast Mass Classificationwith Support Vector Machineand k-Nearest Neighbor Pass the value of k as a variable. Read the Wiconsin BreastCancer DiagnosticCSV file Remove theID columnStratifiedpartitioningStratifiedNode 5Use validationdatasetNode 7Serialize the modelRead the modelUse test datasetFinal scoreNode 12Generate thevalues for kNode 14Node 15Node 16Pull Accuracy columnGet theOverall AccuracyvalueTable with the values of k andaccuracyAdd kto the table10-fold CVCreate and validatethe modelFinal scorePull Accuracy columnGet theOverall AccuracyvalueNode 26Node 27Pull Accuracy columnGet theOverall AccuracyvaluePlot k vs accuracyPull Accuracy columnGet theOverall AccuracyvalueNode 38Sort by Accuracy and kGet the first rowAdd method nameAdd methodnameAdd method nameUnionSUMMARYCSV Reader Column Filter Partitioning X-Partitioner SVM Learner SVM Predictor Scorer PMML Writer PMML Reader SVM Predictor Scorer Normalizer Table Creator Table Row ToVariable Loop Start K Nearest Neighbor Scorer Column Filter Row Filter Loop End Variable toTable Column X-Partitioner K Nearest Neighbor Scorer Column Filter Row Filter X-Aggregator X-Aggregator Column Filter Row Filter Line Plot(JavaScript) Column Filter Row Filter Table Rowto Variable Sorter Row Filter String Manipulation String Manipulation String Manipulation Concatenate(Optional in) Column Resorter

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: adity
Created at: 2019-01-04
On NodePit since: 2019-04-03
Last update: 2024-11-21
Created with KNIME version: v3.7.1

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!