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Regression - House Prices - Advanced Techniques including H2O.ai and Vtreat

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

URL: vtreat for KNIME! https://win-vector.com/2020/06/28/vtreat-for-knime/
URL: H2O.ai AutoML (wrapped with Python) with vtreat data preparation in KNIME for regression problems https://hub.knime.com/-/spaces/-/latest/~QCQZSo2ffXuw5CdZ/
URL: H2O.ai AutoML in KNIME for regression problems https://forum.knime.com/t/h2o-ai-automl-in-knime-for-regression-problems/20924?u=mlauber71
URL: Medium: Data preparation for Machine Learning with KNIME and the Python “vtreat” package https://medium.com/p/efcaf58fa783

# 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 RMSE
https://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 environments
https://medium.com/p/2ac217792539

Medium: Data preparation for Machine Learning with KNIME and the Python “vtreat” package
https://medium.com/p/efcaf58fa783


https://forum.knime.com/t/data-preparation-for-machine-learning-with-knime-and-the-python-vtreat-package/58679?u=mlauber71

KNIME — Machine Learning and Artificial Intelligence— A Collection
https://medium.com/p/12e0f7d83b50

About Machine-Learning — How it Fails and Succeeds
https://medium.com/p/9f3ab7cb9b00

dataset_regression_20.parquet=> to be used in a Jupyter notebookto develop a H2O.ai model
Parquet Writer
collect all modelresults
Concatenate
locate and create /data/ folder with absolute paths
Collect Local Metadata
collect resultsof severalregressionmodelsH2O
Concatenate
dataset_regression_80.parquet
Parquet Writer
collect resultsof severalregressionmodelsKNIME
Concatenate
create initial Test andTraining dataKaggle House Prices: Advanced Regression Techniques
Test Training
dataset_regression_vtreat_20.parquet
Parquet Writer
dataset_regression_vtreat_80.parquet
Parquet Writer
Table Partitioner
dataset_regression.parquethttps://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview
Parquet Reader
collect LightGBM modelresults
Concatenate
20
Table to H2O
80
Row Filter
Domain Calculator
Py_LightGBM
Py_LightGBM
Py_LightGBM_vtreat
Py_LightGBM_vtreat
20
Row Filter
RowID
RMSEASCENDING=> lowest value -> best model
Sorter
model_results.xlsx
Excel Writer
different H2O.ai modelsfor regression tasks
H2O
models withvtreat data preparation
VTREAT
WEKA
F10 - open view
Select Parameters for Models
Merge Variables
Activate Conda Environmentbased on Operating SystemWindows or macOSconfigure how to handle the environmentdefault = just check the names
conda_environment_lightgbm
80
Table to H2O
H2O Local Context
various KNIME modelsfor regression tasks=> "RProp MLP Learner" here
KNIME
collect resultsof severalregressionmodelsVTREAT
Concatenate
collect resultsof severalregressionmodelsWEKA
Concatenate

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: m_lauber
Created at: 2018-12-06
On NodePit since: 2024-07-03
Last update: 2025-09-22
Created with KNIME version: v5.7.0
Tags: h2oh2o.aimachine learningregressionRMSELightGBM

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