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03_​Explain_​Model

3. Explain Model
Replace this node by yourfavourite model learner Replace this node bythe appropriatepredictor Replace the node bythe node thatproduces your desiredscore variable 3. Explain ModelThis example shows one method for determing relative "importance" of each Feature based on the algorythm you have chosen for your model. Many other options and approaches are available depending onthe machine learning technique you use.. In this case, an example is shown using forward feature addition around a random forrest, since that was the method chosen for the prediction problem.For further information, please refer to the white paper "Taking a proactive approach to GDPR with KNIME" Adults.TableChoose the variableto optimizeConfigure your setof columns hereNode 9Node 10capturefeature"importance"informationNode 12 Table Reader Feature SelectionLoop End Partitioning Scorer Feature SelectionLoop Start (1:1) Random ForestLearner Random ForestPredictor Table Writer Column Filter Replace this node by yourfavourite model learner Replace this node bythe appropriatepredictor Replace the node bythe node thatproduces your desiredscore variable 3. Explain ModelThis example shows one method for determing relative "importance" of each Feature based on the algorythm you have chosen for your model. Many other options and approaches are available depending onthe machine learning technique you use.. In this case, an example is shown using forward feature addition around a random forrest, since that was the method chosen for the prediction problem.For further information, please refer to the white paper "Taking a proactive approach to GDPR with KNIME" Adults.TableChoose the variableto optimizeConfigure your setof columns hereNode 9Node 10capturefeature"importance"informationNode 12 Table Reader Feature SelectionLoop End Partitioning Scorer Feature SelectionLoop Start (1:1) Random ForestLearner Random ForestPredictor Table Writer Column Filter

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