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kn_​example_​ml_​regression_​housing_​prices

Score Kaggle House Prices: Advanced Regression Techniques - prepare data with vtreat - use H2O.ai nodes and other models - measure results with RMSE

Score Kaggle House Prices: Advanced Regression Techniques - prepare data with vtreat - use H2O.ai nodes and other models - measure results with RMSE
https://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview

# Run AutoML for 60 seconds or# 300 = 5 min, 600 = 10 min, 900 = 15 min, 1800 = 30 min, 3600 =1 hour, # 7200 = 2 hours# 14400 = 4 hours# 16200 = 4.5 hours# 18000 = 5 Stunden# 21600 = 6 hours# 25200 = 7 hours# 28800 = 8 hours# 36000 = 10 hours Score Kaggle House Prices: Advanced Regression Techniques - prepare data with vtreat - use H2O.ai nodes and other models - measure results with RMSEhttps://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview Python Conda environment propagation. Please read this article for more details:KNIME and Python — Setting up and managing Conda environmentshttps://medium.com/p/2ac217792539 Propagate Python environmentfor KNIME on MacOSX (Apple Scilicon)with Miniforge / Minicondaconfigure how to handle the environmentdefault = just check the names80/20split training/testedit: v_runtime_automlset the maximum runtime ofH2O.ai AutoML in SECONDScreate initial Test andTraining dataKaggle House Prices: Advanced Regression TechniquesPropagate 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 names8020collect resultsof severalmulticlassmodelsRMSEASCENDING=> lowest value -> best modelmodel_results.xlsxdataset_regression.parquethttps://www.kaggle.com/c/house-prices-advanced-regression-techniques/overviewdataset_regression_20.parquet=> to be used in a Jupyter notebookto develop a H2O.ai modeldataset_regression_80.parquetdataset_regression_vtreat_80.parquetdataset_regression_vtreat_20.parquetmodels withvtreat data preparationdifferent H2O.ai modelsfor regression tasksvarious KNIME modelsfor regression tasksconda_environment_kaggle_apple_silicon H2O Local Context Table to H2O H2O Partitioning Integer Input(legacy) Test Training conda_environment_kaggle_macosx conda_environment_kaggle_windows H2O to Table H2O to Table Concatenate Sorter RowID Excel Writer Parquet Reader Parquet Writer Parquet Writer Parquet Writer Parquet Writer VTREAT H2O WEKA KNIME # Run AutoML for 60 seconds or# 300 = 5 min, 600 = 10 min, 900 = 15 min, 1800 = 30 min, 3600 =1 hour, # 7200 = 2 hours# 14400 = 4 hours# 16200 = 4.5 hours# 18000 = 5 Stunden# 21600 = 6 hours# 25200 = 7 hours# 28800 = 8 hours# 36000 = 10 hours Score Kaggle House Prices: Advanced Regression Techniques - prepare data with vtreat - use H2O.ai nodes and other models - measure results with RMSEhttps://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview Python Conda environment propagation. Please read this article for more details:KNIME and Python — Setting up and managing Conda environmentshttps://medium.com/p/2ac217792539 Propagate Python environmentfor KNIME on MacOSX (Apple Scilicon)with Miniforge / Minicondaconfigure how to handle the environmentdefault = just check the names80/20split training/testedit: v_runtime_automlset the maximum runtime ofH2O.ai AutoML in SECONDScreate initial Test andTraining dataKaggle House Prices: Advanced Regression TechniquesPropagate 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 names8020collect resultsof severalmulticlassmodelsRMSEASCENDING=> lowest value -> best modelmodel_results.xlsxdataset_regression.parquethttps://www.kaggle.com/c/house-prices-advanced-regression-techniques/overviewdataset_regression_20.parquet=> to be used in a Jupyter notebookto develop a H2O.ai modeldataset_regression_80.parquetdataset_regression_vtreat_80.parquetdataset_regression_vtreat_20.parquetmodels withvtreat data preparationdifferent H2O.ai modelsfor regression tasksvarious KNIME modelsfor regression tasks conda_environment_kaggle_apple_silicon H2O Local Context Table to H2O H2O Partitioning Integer Input(legacy) Test Training conda_environment_kaggle_macosx conda_environment_kaggle_windows H2O to Table H2O to Table Concatenate Sorter RowID Excel Writer Parquet Reader Parquet Writer Parquet Writer Parquet Writer Parquet Writer VTREAT H2O WEKA KNIME

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Created by: m_lauber
Created at: 2018-12-06
On NodePit since: 2022-12-30
Last update: 2024-04-24
Created with KNIME version: v4.7.0
Tags: h2oh2o.aimachine learningregressionRMSE

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