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kn_​example_​ml_​multiclass_​wine_​quality

Score UCI Wine Quality Dataset - multiple Targets (multiclass) with H2O.ai nodes and other models - measure results with LogLoss

Score UCI Wine Quality Dataset - multiple Targets (multiclass) with H2O.ai nodes and other models - measure results with LogLoss
https://archive.ics.uci.edu/ml/datasets/wine+quality

# 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 UCI Wine Quality Dataset - multiple Targets (multiclass) with H2O.ai nodes and other models - measure results with LogLosshttps://archive.ics.uci.edu/ml/datasets/wine+qualityhttps://forum.knime.com/t/i-have-a-data-set-consisting-of-5-inputs-and-6-outputs-i-entered-it-in-machine-learning-it-gave-me-medium-accuracy-i-want-high-accuracy-can-you-suggest-the-algorithm-to-me/44276 prepare the data with the help of the "vtreat" package 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 SECONDSLogLossH2O_AutoML=> only GBM and Stacked Models4 folds (might change that)H2O_AutoMLGLMGBMNBGBMGBMGBMH2O_AutoMLH2O_AutoMLh2o_gbm.ziph2o_gbm.ziph2o_gbm.ziph2o_automl.zipNBNBNBh2o_nb.ziph2o_nb.zipGLMGLMGLMh2o_glm.ziph2o_glm.zipXGBOOST8020XGBOOSTxgboost.zipxgboost.zipbring resultsback to H2OXGBOOSTXGBOOSTXGBOOSTSMOWEKA_SMOweka_smo.zipweka_smo.zipWEKA_SMOWEKA_SMObring resultsback to H2OWEKA_SMOcollect resultsof severalmulticlassmodelsH2O_DEEP_LEARNINGH2O_DEEP_LEARNINGH2O_DEEP_LEARNINGLogLossH2O_DEEP_LEARNING=> only deep learning Models4 folds (might change that)h2o_automl_deep_learning.ziph2o_automl_deep_learning.zipLogLossASCENDING=> lowest value -> best modelmodel_results.xlsxhttps://archive.ics.uci.edu/ml/datasets/wine+qualitydataset_multiclass.parquethttps://archive.ics.uci.edu/ml/datasets/wine+qualitydataset_multiclass_20.parquet=> to be used in a Jupyter notebookto develop a H2O.ai modeldataset_multiclass_80.parquet80 vtreatLogLossH2O_AutoML=> only GBM and Stacked Models4 folds (might change that)h2o_automl_vtreat.ziph2o_automl_vtreat.zipH2O_AutoML_vtreatH2O_AutoML_vtreatH2O_AutoML_vtreat20"Class"as the multi-class target80 vtreatvtreat for KNIME!https://win-vector.com/2020/06/28/vtreat-for-knime/"Class"as the multi-class target20 vtreatAPPLYh2o_automl_vtreat_treatment.ziph2o_automl_vtreat_treatment.zipPropagate Python environmentfor KNIME on Windows withMiniforge / Minicondaconfigure how to handle the environmentdefault = just check the namesconda_environment_kaggle_apple_silicon H2O Local Context Table to H2O H2O Partitioning Integer Input(legacy) H2O AutoML Learner H2O Predictor(Classification) H2O GeneralizedLinear Model Learner H2O Gradient BoostingMachine Learner H2O NaiveBayes Learner H2O Predictor(Classification) H2O MultinomialScorer ConstantValue Column H2O MultinomialScorer ConstantValue Column Model Reader Model Writer Model Reader Model Writer H2O MultinomialScorer ConstantValue Column H2O Predictor(Classification) Model Reader Model Writer H2O MultinomialScorer ConstantValue Column H2O Predictor(Classification) Model Reader Model Writer XGBoost TreeEnsemble Learner H2O to Table H2O to Table XGBoost Predictor Model Reader Model Writer Table to H2O H2O MultinomialScorer ConstantValue Column SMO (3.7) Weka Predictor(3.7) Weka ClassifierWriter (3.7) Weka ClassifierReader (3.7) H2O MultinomialScorer ConstantValue Column Table to H2O WEKA_LOGIT WEKA_ADA Concatenate H2O MultinomialScorer H2O Predictor(Classification) ConstantValue Column H2O AutoML Learner Model Reader Model Writer Sorter RowID Excel Writer Prepare UCIWine Quality Parquet Reader Parquet Writer Parquet Writer Table to H2O H2O AutoML Learner Model Reader Model Writer H2O MultinomialScorer H2O Predictor(Classification) ConstantValue Column Table to H2O Python Script Python Script Model Reader Model Writer conda_environment_kaggle_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 Score UCI Wine Quality Dataset - multiple Targets (multiclass) with H2O.ai nodes and other models - measure results with LogLosshttps://archive.ics.uci.edu/ml/datasets/wine+qualityhttps://forum.knime.com/t/i-have-a-data-set-consisting-of-5-inputs-and-6-outputs-i-entered-it-in-machine-learning-it-gave-me-medium-accuracy-i-want-high-accuracy-can-you-suggest-the-algorithm-to-me/44276 prepare the data with the help of the "vtreat" package 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 SECONDSLogLossH2O_AutoML=> only GBM and Stacked Models4 folds (might change that)H2O_AutoMLGLMGBMNBGBMGBMGBMH2O_AutoMLH2O_AutoMLh2o_gbm.ziph2o_gbm.ziph2o_gbm.ziph2o_automl.zipNBNBNBh2o_nb.ziph2o_nb.zipGLMGLMGLMh2o_glm.ziph2o_glm.zipXGBOOST8020XGBOOSTxgboost.zipxgboost.zipbring resultsback to H2OXGBOOSTXGBOOSTXGBOOSTSMOWEKA_SMOweka_smo.zipweka_smo.zipWEKA_SMOWEKA_SMObring resultsback to H2OWEKA_SMOcollect resultsof severalmulticlassmodelsH2O_DEEP_LEARNINGH2O_DEEP_LEARNINGH2O_DEEP_LEARNINGLogLossH2O_DEEP_LEARNING=> only deep learning Models4 folds (might change that)h2o_automl_deep_learning.ziph2o_automl_deep_learning.zipLogLossASCENDING=> lowest value -> best modelmodel_results.xlsxhttps://archive.ics.uci.edu/ml/datasets/wine+qualitydataset_multiclass.parquethttps://archive.ics.uci.edu/ml/datasets/wine+qualitydataset_multiclass_20.parquet=> to be used in a Jupyter notebookto develop a H2O.ai modeldataset_multiclass_80.parquet80 vtreatLogLossH2O_AutoML=> only GBM and Stacked Models4 folds (might change that)h2o_automl_vtreat.ziph2o_automl_vtreat.zipH2O_AutoML_vtreatH2O_AutoML_vtreatH2O_AutoML_vtreat20"Class"as the multi-class target80 vtreatvtreat for KNIME!https://win-vector.com/2020/06/28/vtreat-for-knime/"Class"as the multi-class target20 vtreatAPPLYh2o_automl_vtreat_treatment.ziph2o_automl_vtreat_treatment.zipPropagate Python environmentfor KNIME on Windows withMiniforge / Minicondaconfigure how to handle the environmentdefault = just check the names conda_environment_kaggle_apple_silicon H2O Local Context Table to H2O H2O Partitioning Integer Input(legacy) H2O AutoML Learner H2O Predictor(Classification) H2O GeneralizedLinear Model Learner H2O Gradient BoostingMachine Learner H2O NaiveBayes Learner H2O Predictor(Classification) H2O MultinomialScorer ConstantValue Column H2O MultinomialScorer ConstantValue Column Model Reader Model Writer Model Reader Model Writer H2O MultinomialScorer ConstantValue Column H2O Predictor(Classification) Model Reader Model Writer H2O MultinomialScorer ConstantValue Column H2O Predictor(Classification) Model Reader Model Writer XGBoost TreeEnsemble Learner H2O to Table H2O to Table XGBoost Predictor Model Reader Model Writer Table to H2O H2O MultinomialScorer ConstantValue Column SMO (3.7) Weka Predictor(3.7) Weka ClassifierWriter (3.7) Weka ClassifierReader (3.7) H2O MultinomialScorer ConstantValue Column Table to H2O WEKA_LOGIT WEKA_ADA Concatenate H2O MultinomialScorer H2O Predictor(Classification) ConstantValue Column H2O AutoML Learner Model Reader Model Writer Sorter RowID Excel Writer Prepare UCIWine Quality Parquet Reader Parquet Writer Parquet Writer Table to H2O H2O AutoML Learner Model Reader Model Writer H2O MultinomialScorer H2O Predictor(Classification) ConstantValue Column Table to H2O Python Script Python Script Model Reader Model Writer conda_environment_kaggle_windows

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Created by: m_lauber
Created at: 2018-12-06
On NodePit since: 2022-06-22
Last update: 2024-12-21
Created with KNIME version: v4.7.0
Tags: h2oh2o.aimulticlassmachine learninglogloss

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