Nashville_meetup_demo
The workflow describes a simple use case how Neo4j Graph Data Science and Knime algorithms can be used together.
prototype
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Training_Workflow1
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Python Transform
This Component transforms values of the user selected columns to normalized values by standardizing them across their mean values. The Component uses the […]
Python Transform
This Component transforms values of the user selected columns to normalized values by standardizing them across their mean values. The Component uses the […]
01_Train_and_Explain_Keras_Network_with_Counterfactuals
This application is a simple example of using Conterfactual Explanations (Python) Component to identify the counterfactual instances for a Binary […]
Conformal prediction classification
This demo describes how to enrich the classification workflow with conformal prediction methods that allows to estimate model prediction certainity and […]
07_Group_By
This workflow shows the use of the GroupBy Node in the context of Image Processing where it can be used to merge individual segments into an entire image […]
kn_automl_h2o_regression_python_vtreat
H2O.ai AutoML (wrapped with Python) with vtreat data preparation in KNIME for regression problems (with R vtreat) - a powerful auto-machine-learning […]
ML_Algorithms_DecisionTreeClassifier
This is an example workflow to test Simple Machine learning classification algorithms on daily weather data set. To Predict => Low Humid Day or Not