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kn_​automl_​h2o_​classification

H2O.ai AutoML (generic KNIME nodes) in KNIME for classification problems - a powerful auto-machine-learning framework

H2O.ai AutoML (generic KNIME nodes) in KNIME for classification problems - a powerful auto-machine-learning framework (https://hub.knime.com/mlauber71/spaces/Public/latest/automl/)
v 1.75

It features various models like Random Forest along with Deep Learning. The results will be written to a folder and the models will be stored in MOJO format to be used in KNIME (as well as on a Big Data cluster via Sparkling Water). One major parameter to set is the running time the model has to test various models and do some hyper parameter optimization as well. The best model of each round is stored and some graphics are produced to see the results.

To run the validations in this workflow you have to install R with several packages. Please refer to the green box on the right.

The results may be used also on Big Data clusters with the help of H2O.ai Sparkling Water (https://hub.knime.com/mlauber71/spaces/Public/latest/kn_example_h2o_sparkling_water)

# 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 H2O.ai AutoML (generic KNIME nodes) in KNIME for classification problems - a powerful auto-machine-learning framework (https://hub.knime.com/mlauber71/spaces/Public/latest/automl/)v 1.75It features various models like Random Forest along with Deep Learning. The results will be written to a folder and the models will be stored in MOJO format to be used in KNIME (as well as on a Big Data cluster via Sparkling Water). One major parameter to set is the running time the model has to test various models and do some hyper parameter optimization as well. The best model of each round is stored and some graphics are produced to see theresults.To run the validations in this workflow you have to install R with several packages. Please refer to the green box on the right.The results may be used also on Big Data clusters with the help of H2O.ai Sparkling Water (https://hub.knime.com/mlauber71/spaces/Public/latest/kn_example_h2o_sparkling_water) which output is there to be interpretedmodel are stored in the folder /model/<full model name>/<model name>.zip-> as MOJO model format (certain model types cannot be stored and reused - so they are excluded as of now)/model/validate/h2o_list_of_models.csv -> list of all leading model from the runs with their RMSE (among other things) --- individual model results/model/validate/model_table_H2O_AutoML_Classification_yyyymmdd_hhmmh.table-> a KNIME table with a collection of parameters and information about the modelH2O_AutoML_Classification_yyyymmdd_hhmmh_-> CSVfiles containing important information among these: - _leaderboard = the list of all tested models in the runH2O_AutoML_Classification_yyyymmdd_hhmmh.xlsx-> an Excel file containing important information among these:- model_cutoff = check the best cutoff for your business case (depending on the number of different scores you will getthe scores rounded to 0.1 or 0.10) (max_cohens_kappa = based on best Cohen's Cappa, max_f_measure = based on best F1 score)- model_cutoff_overview = compact overview of cut-off results---- 4 graphics for each model to have visual support when interpreting the results (needs R)model_graph_H2O_AutoML_Class_yyyymmdd_hhmmh_cutoff.png-> a graphic illustrating the consequences of two possible cut-offs (with statistics). Please note depending on yourbusiness needs you might choose completely different onesmodel_graph_H2O_AutoML_Class_yyyymmdd_hhmmh_roc.png-> a classic ROC (receiver operating characteristic) curve with statisticsmodel_graph_H2O_AutoML_Class_yyyymmdd_hhmmh_lift.png-> a classic lift curve with statistics. Illustrating how the TOP 10% of your score are doing compared to the restmodel_graph_H2O_AutoML_Class_yyyymmdd_hhmmh_ks.png-> two curves illustrating the Kolmogorov-Smirnov Goodness-of-Fit Test Subfolders to check/data/ contains the original data/model/contains the stored models in MOJO and H2O format/model/validate/contains the validations and graphics/script/a PDF with further informations about the methods usedH2O.ai AutoML in KNIME for classification problems.pdf # make sure you have R and the necessary R packages installed, also check aout the pdf in /script/https://hub.knime.com/mlauber71/spaces/Public/latest/_r_installation_on_knime_collection~tj5tS_6gYvqOSPlk# Install R alongside KNIME on Windows and MacOS# https://forum.knime.com/t/install-r-alongside-knime-on-windows-and-macos/13287# R and Rtools# https://forum.knime.com/t/how-to-import-tables-from-docx-documents-via-r-snippet/19284/10# RServe 1.8.6+ on MacOSX# https://forum.knime.com/t/installing-rserve-1-8-6-on-macos-10-15-catalina/20909/6?u=mlauber71# if you wish to use the 'pure' R code and import the data with parquetlibrary(arrow) additional R packages needed:ggplot2, lift, reshape2http://docs.h2o.ai/h2o/latest-stable/h2o-docs/downloading.html Inspect the models so far and see to results. This will also give you a quick idea where you stand and what you would be able to achieve.Along with all parameters to load the respective model. Propagate R environmentfor KNIME on MacOS withMiniforge / Minicondaconfigure how to handle the environmentdefault = just check the namesvar_model_pathcreate initial Test andTraining dataCensus incomeclassificationtrain.tabletest_solution_submission.parquetedit: v_runtime_automlset the maximum runtime ofH2O.ai AutoML in SECONDSexclude pathsh2o_list_of_models.csvappend if CSV already exists to collect allmodel runsRead the MOJOmodelyou could check out this nodeScore the test tableyou might also use a third table to validatethat has not been used developing themodelvar_model_name_fullsolutionto string^(.*submission|solution).*$h2o_list_of_models.csvAUC DESCkeep best modelRead the MOJOmodelwrite the mojo modelFirst row (best model)var_model_pathvar_model_name_fullvar_leaderboard_pathvar_leaderboard_pathLeaderboardvar_leaderboard_pathv_model_pathtest.tablebinary classification modelswith RPropagate R environmentfor KNIME on Windows withMiniforge / Minicondaconfigure how to handle the environmentdefault = just check the namesknime_r_environment Java EditVariable (simple) Test Training Table Reader Parquet Writer Integer Input collect meta data Merge Variables RowID Column Filter CSV Writer Column Resorter H2O MOJO Reader Binary ClassificationInspector H2O MOJO Predictor(Classification) ROC Curve (local) ConstantValue Column Number To String Column Filter Column Rename CSV Reader String to Path(Variable) Sorter Row Filter Column Filter Table Rowto Variable H2O MOJO Reader H2O AutoML Learner H2O Local Context Table to H2O H2O Model to MOJO H2O MOJO Writer Row Filter Table Rowto Variable String to Path(Variable) String Manipulation Joiner Java EditVariable (simple) String to Path(Variable) CSV Writer ConstantValue Column Merge Variables ConstantValue Column Table Reader Model QualityClassification - Graphics knime_r_environment_windows # 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 H2O.ai AutoML (generic KNIME nodes) in KNIME for classification problems - a powerful auto-machine-learning framework (https://hub.knime.com/mlauber71/spaces/Public/latest/automl/)v 1.75It features various models like Random Forest along with Deep Learning. The results will be written to a folder and the models will be stored in MOJO format to be used in KNIME (as well as on a Big Data cluster via Sparkling Water). One major parameter to set is the running time the model has to test various models and do some hyper parameter optimization as well. The best model of each round is stored and some graphics are produced to see theresults.To run the validations in this workflow you have to install R with several packages. Please refer to the green box on the right.The results may be used also on Big Data clusters with the help of H2O.ai Sparkling Water (https://hub.knime.com/mlauber71/spaces/Public/latest/kn_example_h2o_sparkling_water) which output is there to be interpretedmodel are stored in the folder /model/<full model name>/<model name>.zip-> as MOJO model format (certain model types cannot be stored and reused - so they are excluded as of now)/model/validate/h2o_list_of_models.csv -> list of all leading model from the runs with their RMSE (among other things) --- individual model results/model/validate/model_table_H2O_AutoML_Classification_yyyymmdd_hhmmh.table-> a KNIME table with a collection of parameters and information about the modelH2O_AutoML_Classification_yyyymmdd_hhmmh_-> CSVfiles containing important information among these: - _leaderboard = the list of all tested models in the runH2O_AutoML_Classification_yyyymmdd_hhmmh.xlsx-> an Excel file containing important information among these:- model_cutoff = check the best cutoff for your business case (depending on the number of different scores you will getthe scores rounded to 0.1 or 0.10) (max_cohens_kappa = based on best Cohen's Cappa, max_f_measure = based on best F1 score)- model_cutoff_overview = compact overview of cut-off results---- 4 graphics for each model to have visual support when interpreting the results (needs R)model_graph_H2O_AutoML_Class_yyyymmdd_hhmmh_cutoff.png-> a graphic illustrating the consequences of two possible cut-offs (with statistics). Please note depending on yourbusiness needs you might choose completely different onesmodel_graph_H2O_AutoML_Class_yyyymmdd_hhmmh_roc.png-> a classic ROC (receiver operating characteristic) curve with statisticsmodel_graph_H2O_AutoML_Class_yyyymmdd_hhmmh_lift.png-> a classic lift curve with statistics. Illustrating how the TOP 10% of your score are doing compared to the restmodel_graph_H2O_AutoML_Class_yyyymmdd_hhmmh_ks.png-> two curves illustrating the Kolmogorov-Smirnov Goodness-of-Fit Test Subfolders to check/data/ contains the original data/model/contains the stored models in MOJO and H2O format/model/validate/contains the validations and graphics/script/a PDF with further informations about the methods usedH2O.ai AutoML in KNIME for classification problems.pdf # make sure you have R and the necessary R packages installed, also check aout the pdf in /script/https://hub.knime.com/mlauber71/spaces/Public/latest/_r_installation_on_knime_collection~tj5tS_6gYvqOSPlk# Install R alongside KNIME on Windows and MacOS# https://forum.knime.com/t/install-r-alongside-knime-on-windows-and-macos/13287# R and Rtools# https://forum.knime.com/t/how-to-import-tables-from-docx-documents-via-r-snippet/19284/10# RServe 1.8.6+ on MacOSX# https://forum.knime.com/t/installing-rserve-1-8-6-on-macos-10-15-catalina/20909/6?u=mlauber71# if you wish to use the 'pure' R code and import the data with parquetlibrary(arrow) additional R packages needed:ggplot2, lift, reshape2http://docs.h2o.ai/h2o/latest-stable/h2o-docs/downloading.html Inspect the models so far and see to results. This will also give you a quick idea where you stand and what you would be able to achieve.Along with all parameters to load the respective model. Propagate R environmentfor KNIME on MacOS withMiniforge / Minicondaconfigure how to handle the environmentdefault = just check the namesvar_model_pathcreate initial Test andTraining dataCensus incomeclassificationtrain.tabletest_solution_submission.parquetedit: v_runtime_automlset the maximum runtime ofH2O.ai AutoML in SECONDSexclude pathsh2o_list_of_models.csvappend if CSV already exists to collect allmodel runsRead the MOJOmodelyou could check out this nodeScore the test tableyou might also use a third table to validatethat has not been used developing themodelvar_model_name_fullsolutionto string^(.*submission|solution).*$h2o_list_of_models.csvAUC DESCkeep best modelRead the MOJOmodelwrite the mojo modelFirst row (best model)var_model_pathvar_model_name_fullvar_leaderboard_pathvar_leaderboard_pathLeaderboardvar_leaderboard_pathv_model_pathtest.tablebinary classification modelswith RPropagate R environmentfor KNIME on Windows withMiniforge / Minicondaconfigure how to handle the environmentdefault = just check the namesknime_r_environment Java EditVariable (simple) Test Training Table Reader Parquet Writer Integer Input collect meta data Merge Variables RowID Column Filter CSV Writer Column Resorter H2O MOJO Reader Binary ClassificationInspector H2O MOJO Predictor(Classification) ROC Curve (local) ConstantValue Column Number To String Column Filter Column Rename CSV Reader String to Path(Variable) Sorter Row Filter Column Filter Table Rowto Variable H2O MOJO Reader H2O AutoML Learner H2O Local Context Table to H2O H2O Model to MOJO H2O MOJO Writer Row Filter Table Rowto Variable String to Path(Variable) String Manipulation Joiner Java EditVariable (simple) String to Path(Variable) CSV Writer ConstantValue Column Merge Variables ConstantValue Column Table Reader Model QualityClassification - Graphics knime_r_environment_windows

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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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Created by: m_lauber
Created at: 2020-01-23
On NodePit since: 2021-07-02
Last update: 2024-04-18
Created with KNIME version: v4.6.3
Tags: h2oautomlknimexgboostrrstudioautomlautomaticmachinelearningh2o.ai

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