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Exercise2

KBL: Chapter 4 Exercise2
Workflow: Chapter 4/Exercise 2 This is an example of how to change the classification thresholds.In the previous exercise some patterns were not correctly classified. By applyinga different set of thresholds to the output probabilities, the final wine classification can be improved.The key node of this exercise is the Rule Engine at the end, defining a set of thresholds for a more exact classification. 80% vs. 20%Node 9more conservative class.if pred class > 0.7 => class 3if pred class < 0.3 => class 1otherwise class 2normalization in [0,1]14-10-3Node 14Class backto 1,2,3Class to IntClass to Stringclassvspredictionwine.data filewith path relative to"current workflow" Partitioning MultiLayerPerceptronPredictor Rule Engine Normalizer RProp MLP Learner Normalizer (Apply) Denormalizer Double To Int String Manipulation Scorer (JavaScript) CSV Reader Workflow: Chapter 4/Exercise 2 This is an example of how to change the classification thresholds.In the previous exercise some patterns were not correctly classified. By applyinga different set of thresholds to the output probabilities, the final wine classification can be improved.The key node of this exercise is the Rule Engine at the end, defining a set of thresholds for a more exact classification. 80% vs. 20%Node 9more conservative class.if pred class > 0.7 => class 3if pred class < 0.3 => class 1otherwise class 2normalization in [0,1]14-10-3Node 14Class backto 1,2,3Class to IntClass to Stringclassvspredictionwine.data filewith path relative to"current workflow"Partitioning MultiLayerPerceptronPredictor Rule Engine Normalizer RProp MLP Learner Normalizer (Apply) Denormalizer Double To Int String Manipulation Scorer (JavaScript) CSV Reader

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On NodePit since: 2021-02-21
Last update: 2024-12-23
Created with KNIME version: v4.3.1
Tags: EducationKNIMEPressMLPPartitionNormalizationScorer

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