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BIA505_​midtermProjectClassification

Reading data csv fileRemove column0 & native.country featuresNode 3EDA and missing valuesMissing valueshandlingstrategyImpute missing valuesfor training setImpute missing valuesfor validation setLabel encoder fornominal featuressConvert categorical tonumeric for the training setConvert categorical tonumeric for the holdoutvalidation setNode 12Node 13Node 14Node 16Node 18Node 19CSV Reader Column Filter Feature SelectionLoop Start (1:1) Data Explorer Missing Value Missing Value(Apply) Missing Value(Apply) Category To Number Category ToNumber (Apply) Category ToNumber (Apply) Random ForestLearner Random ForestPredictor Scorer Feature SelectionFilter Feature SelectionLoop End Partitioning Reading data csv fileRemove column0 & native.country featuresNode 3EDA and missing valuesMissing valueshandlingstrategyImpute missing valuesfor training setImpute missing valuesfor validation setLabel encoder fornominal featuressConvert categorical tonumeric for the training setConvert categorical tonumeric for the holdoutvalidation setNode 12Node 13Node 14Node 16Node 18Node 19CSV Reader Column Filter Feature SelectionLoop Start (1:1) Data Explorer Missing Value Missing Value(Apply) Missing Value(Apply) Category To Number Category ToNumber (Apply) Category ToNumber (Apply) Random ForestLearner Random ForestPredictor Scorer Feature SelectionFilter Feature SelectionLoop End Partitioning

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Created by: joudial-fattal
Created at: 2022-05-01
On NodePit since: 2022-05-01
Last update: 2024-04-26
Created with KNIME version: v4.5.2

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