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1. Data Preparation

KBL: Data Preparation for Classification
Workflow: Data Preparation This workflow prepares the data for the next workflow (My First Data Model)and uses some of the most common data preparations:- subsetting (Row Sampling and Partitioning nodes)- Strategies to deal with missing values (Missing Value node)- Shuffling (Shuffle node)- Concatenation of data sets (Concatenate node)- Normalization (Normalizer and Normalizer (Apply) nodes) 20% subsetrandomly drawn with seed50% training setdrawn with linear samplingno seedtraining set + test setbuild normalization lawon training dataapply normalization transformationbuilt on training set to test setmissing values in:age -> mean valueincome -> remove rowadult.data filewith path relative to"current workflow"training settest set Row Sampling Partitioning Shuffle Concatenate Normalizer Normalizer (Apply) Missing Value CSV Reader CSV Writer CSV Writer Workflow: Data Preparation This workflow prepares the data for the next workflow (My First Data Model)and uses some of the most common data preparations:- subsetting (Row Sampling and Partitioning nodes)- Strategies to deal with missing values (Missing Value node)- Shuffling (Shuffle node)- Concatenation of data sets (Concatenate node)- Normalization (Normalizer and Normalizer (Apply) nodes) 20% subsetrandomly drawn with seed50% training setdrawn with linear samplingno seedtraining set + test setbuild normalization lawon training dataapply normalization transformationbuilt on training set to test setmissing values in:age -> mean valueincome -> remove rowadult.data filewith path relative to"current workflow"training settest setRow Sampling Partitioning Shuffle Concatenate Normalizer Normalizer (Apply) Missing Value CSV Reader CSV Writer CSV Writer

Nodes

Extensions

Links

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

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On NodePit since: 2021-02-21
Last update: 2024-11-14
Created with KNIME version: v4.3.1
Tags: KNIMEPressEducationMissing valueNormalizationPartitionShuffle

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