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Auto feature generation workflow

Auto feature engineering workflow
Last amended: 20th Oct, 2019Feature Generator using 'autofeat' libraryRef: https://github.com/knime/knime-pythonWhen data is large, create a model with a sample of data and then feedthe whole data in the model to get features WORKFLOW-I WORKFLOW--II(large data) When data is large:i) Take a data sample. Run auto feature generator-I and create new features.ii) If generated features are OK, save the feature generator model to a file on disk.iii) Next read the complete dataset.iv) Use auto feature generator-II to read earlier saved model from disk and apply it on fulldataset to create new features. Input datafilter_out categoricalcolumnsnormalizenumerica dataNode 20Node 25Node 27Node 28Model savingtrain:testpartitionNode 32Node 33 auto featuregenerator-II CSV Reader Column Filter Normalizer Python ObjectWriter Column Filter Column Filter Normalizer Python ObjectWriter Partitioning Row Sampling CSV Reader auto featuregenerator-I auto featuregenerator-II auto featuregenerator-I Last amended: 20th Oct, 2019Feature Generator using 'autofeat' libraryRef: https://github.com/knime/knime-pythonWhen data is large, create a model with a sample of data and then feedthe whole data in the model to get features WORKFLOW-I WORKFLOW--II(large data) When data is large:i) Take a data sample. Run auto feature generator-I and create new features.ii) If generated features are OK, save the feature generator model to a file on disk.iii) Next read the complete dataset.iv) Use auto feature generator-II to read earlier saved model from disk and apply it on fulldataset to create new features. Input datafilter_out categoricalcolumnsnormalizenumerica dataNode 20Node 25Node 27Node 28Model savingtrain:testpartitionNode 32Node 33 auto featuregenerator-II CSV Reader Column Filter Normalizer Python ObjectWriter Column Filter Column Filter Normalizer Python ObjectWriter Partitioning Row Sampling CSV Reader auto featuregenerator-I auto featuregenerator-II auto featuregenerator-I

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