Simple example to make a random forest model with new Python Scrip in KNIME 4.5 using the iris dataset. Saving and reusing the model with Pickle
Also creating some graphics and exporting them to disk. Via Python code or via KNIME ports. It is not really necessary to do all this with the colourful ports, just to check how it does work
The sub-folder /data/ conatins "py39_knime_2022.yaml" and "py38_knime_2022.yaml" files to create the Python environments if it would not be installed automaticall by the Conda Environment Propagation. Pleas also check out the additional links provided.
Workflow
Simple example to make a random forest model with new Python Scrip in KNIME 4.5 using the iris dataset. Saving and reusing the model with Pickle
External resources
- Python & KNIME (the official guide)
- KNIME & Python (the short story)
- Meta Collection about KNIME and Python
- (forum.knime.com) RUNNING KNIME USING PYTHON [KNIMEPY]
- Pandas Tutor visualizes how your Python code transforms dataframes
- KNIME columnar storage
- What's New in KNIME Analytics Platform 4.5 - Integrating Python & KNIME
- use the new (KNIME 4.5) Python Script node to read Parquet file into KNIME, export it again, put it into SQLite database and read it back
- (forum entry) KNIME 4.5 release, problems with conda.environment variable
- envconfigs - KNIME Python Integration (YML files)
- KNIME Python Integration - envconfigs
- KNIME and Jupyter notebooks and .PY modules
- KNIME and YML configuration with Miniconda (1/2)
- KNIME and YML configuration with Miniconda (2/2)
Used extensions & nodes
Created with KNIME Analytics Platform version 4.5.1
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