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kn_​example_​ml_​vtreat_​binary_​class_​data_​prep_​unsupervised

Prepare (unsupervised) data for machine-learning models with "BINARY" (0,1) Targets using the vtreat package and Python

Prepare (unsupervised) data for machine-learning models with "BINARY" (0,1) Targets using the vtreat package and Python

Since the data preparation with vtreat with a target will 'leak' information into the preparation Which is the purpose and will be OK if you expect the variables and target to have the same systematic connection in the future - wen will try to do a target-agnostic data preparation.

Census Income Data Set
Predict whether income exceeds $50K/yr based on census data. Also known as "Adult" dataset
https://archive.ics.uci.edu/ml/datasets/census+income

create an unsupervised data preparation with vtreat package on the training data, store the procedure and apply it to the test data Python Conda environment propagation. Please read this article for more details:KNIME and Python — Setting up and managing Conda environmentshttps://medium.com/p/2ac217792539 Prepare (unsupervised) data for machine-learning models with "BINARY" (0,1) Targets using the vtreat package and PythonSince the data preparation with vtreat with a target will 'leak' information into the preparation Which is the purpose and will be OK if you expect the variables and target to have the same systematic connection in the future - wen will try to do a target-agnostic data preparation.Census Income Data SetPredict whether income exceeds $50K/yr based on census data. Also known as "Adult" datasethttps://archive.ics.uci.edu/ml/datasets/census+income Medium: Data preparation for Machine Learning with KNIME and the Python “vtreat” packagehttps://medium.com/p/efcaf58fa783https://forum.knime.com/t/data-preparation-for-machine-learning-with-knime-and-the-python-vtreat-package/58679?u=mlauber71 v_vtreat_indicator_min_fraction=> edit!return 0.025;https://github.com/WinVector/pyvtreat/blob/main/Examples/Classification/Classification.mdPropagate Python environmentfor KNIME on MacOSX withMiniforge / Minicondaconfigure how to handle the environmentdefault = just check the namesPropagate Python environmentfor KNIME on Windows withMiniforge / Minicondaconfigure how to handle the environmentdefault = just check the namesdataset_binary_class.parquethttps://archive.ics.uci.edu/ml/datasets/census+income"Target"as the binary targetTEST vtreatPropagate Python environmentfor KNIME on MacOSX (Apple Scilicon)with Miniforge / Minicondaconfigure how to handle the environmentdefault = just check the namesTRAINING / TESTvtreat_treatment_unsupervised.zipvtreat_treatment_unsupervised.zipvtreat_treatment_unsupervised.csvno vtreatvtreatno vtreatvtreatAUC Prdescendingno vtreatno vtreatvtreatvtreatno vtreatno vtreatno vtreatvtreatvtreatvtreat" Target" as the regression target80 vtreatvtreat for KNIME!https://win-vector.com/2020/06/28/vtreat-for-knime/Java EditVariable (simple) conda_environment_kaggle_macosx conda_environment_kaggle_windows Parquet Reader Python Script conda_environment_kaggle_apple_silicon Partitioning Model Reader Model Writer CSV Writer ConstantValue Column ConstantValue Column Concatenate RowID RowID Sorter XGBoost TreeEnsemble Learner XGBoost Predictor XGBoost Predictor XGBoost TreeEnsemble Learner H2O Local Context Table to H2O H2O Binomial Scorer Column Filter ReferenceColumn Filter Table to H2O H2O Binomial Scorer Python Script Merge Variables create an unsupervised data preparation with vtreat package on the training data, store the procedure and apply it to the test data Python Conda environment propagation. Please read this article for more details:KNIME and Python — Setting up and managing Conda environmentshttps://medium.com/p/2ac217792539 Prepare (unsupervised) data for machine-learning models with "BINARY" (0,1) Targets using the vtreat package and PythonSince the data preparation with vtreat with a target will 'leak' information into the preparation Which is the purpose and will be OK if you expect the variables and target to have the same systematic connection in the future - wen will try to do a target-agnostic data preparation.Census Income Data SetPredict whether income exceeds $50K/yr based on census data. Also known as "Adult" datasethttps://archive.ics.uci.edu/ml/datasets/census+income Medium: Data preparation for Machine Learning with KNIME and the Python “vtreat” packagehttps://medium.com/p/efcaf58fa783https://forum.knime.com/t/data-preparation-for-machine-learning-with-knime-and-the-python-vtreat-package/58679?u=mlauber71 v_vtreat_indicator_min_fraction=> edit!return 0.025;https://github.com/WinVector/pyvtreat/blob/main/Examples/Classification/Classification.mdPropagate Python environmentfor KNIME on MacOSX withMiniforge / Minicondaconfigure how to handle the environmentdefault = just check the namesPropagate Python environmentfor KNIME on Windows withMiniforge / Minicondaconfigure how to handle the environmentdefault = just check the namesdataset_binary_class.parquethttps://archive.ics.uci.edu/ml/datasets/census+income"Target"as the binary targetTEST vtreatPropagate Python environmentfor KNIME on MacOSX (Apple Scilicon)with Miniforge / Minicondaconfigure how to handle the environmentdefault = just check the namesTRAINING / TESTvtreat_treatment_unsupervised.zipvtreat_treatment_unsupervised.zipvtreat_treatment_unsupervised.csvno vtreatvtreatno vtreatvtreatAUC Prdescendingno vtreatno vtreatvtreatvtreatno vtreatno vtreatno vtreatvtreatvtreatvtreat" Target" as the regression target80 vtreatvtreat for KNIME!https://win-vector.com/2020/06/28/vtreat-for-knime/Java EditVariable (simple) conda_environment_kaggle_macosx conda_environment_kaggle_windows Parquet Reader Python Script conda_environment_kaggle_apple_silicon Partitioning Model Reader Model Writer CSV Writer ConstantValue Column ConstantValue Column Concatenate RowID RowID Sorter XGBoost TreeEnsemble Learner XGBoost Predictor XGBoost Predictor XGBoost TreeEnsemble Learner H2O Local Context Table to H2O H2O Binomial Scorer Column Filter ReferenceColumn Filter Table to H2O H2O Binomial Scorer Python Script Merge Variables

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Created by: m_lauber
Created at: 2022-12-29
On NodePit since: 2023-01-01
Last update: 2024-04-27
Created with KNIME version: v4.7.0
Tags: vtreatdatapreparebinary

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