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04_​Active_​Learning_​with_​basic_​SVM

Active Learning with Basic SVM Model
Active learning strategy for labeling Train SVM on randomly selected data points Train model on data points selected via activelearning Compare Strategies- Right click the component- "Execute and Open Views"To open the component:- Right click the component- Component > Open Component Random selection strategy for labeling Active Learning with Basic SVM Model: This workflow uses a simple example to demonstrate one possible structure for an active learningapplication and compares the effectiveness of the active learning strategy vs a random labeling approach. calculate denselyrepresented areasin data setselect 3 data points for initializationupdate densitywith labeled pointsoutputs 10labeled points( 7 iterations + 3 initialization rows )combine with setalready labeledpointstrain final modelon active learning labeleddata pointsscore unlabeleddata points10 random pointsfor labelingmodel trainedon same number of data pointsbut randomly selectedscore unlabeleddata pointsGenerates datafor use in examplelabel a fewrandom pointsto initiatelabel top 1most "valued"data pointslabel random data points Potential DensityInitializer Active LearningLoop Start Row Sampling Density Updater Active LearningLoop End Concatenate SVM Learner SVM Predictor ReferenceRow Filter Partitioning SVM Learner SVM Predictor Compare Strategies Data Generation Column Expressions Column Expressions Column Expressions Active LearningSampling Active learning strategy for labeling Train SVM on randomly selected data points Train model on data points selected via activelearning Compare Strategies- Right click the component- "Execute and Open Views"To open the component:- Right click the component- Component > Open Component Random selection strategy for labeling Active Learning with Basic SVM Model: This workflow uses a simple example to demonstrate one possible structure for an active learningapplication and compares the effectiveness of the active learning strategy vs a random labeling approach. calculate denselyrepresented areasin data setselect 3 data points for initializationupdate densitywith labeled pointsoutputs 10labeled points( 7 iterations + 3 initialization rows )combine with setalready labeledpointstrain final modelon active learning labeleddata pointsscore unlabeleddata points10 random pointsfor labelingmodel trainedon same number of data pointsbut randomly selectedscore unlabeleddata pointsGenerates datafor use in examplelabel a fewrandom pointsto initiatelabel top 1most "valued"data pointslabel random data pointsPotential DensityInitializer Active LearningLoop Start Row Sampling Density Updater Active LearningLoop End Concatenate SVM Learner SVM Predictor ReferenceRow Filter Partitioning SVM Learner SVM Predictor Compare Strategies Data Generation Column Expressions Column Expressions Column Expressions Active LearningSampling

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Created by: Corey
Created at: 2019-11-20
On NodePit since: 2020-08-04
Last update: 2024-12-21
Created with KNIME version: v4.1.0
Tags: active learningpotential densityuncertaintyexploration/exploitationSVMexplorationexploitationPBACbinary classification inspectorscatter plotinteractivecomponentconfusion matrixROCexample

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