Iconkn_​automl_​h2o_​regression_​r 

H2O.ai AutoML (wrapped with R) in KNIME for regression problems - a powerful auto-machine-learning framework […]

Iconkn_​automl_​h2o_​regression_​r 

H2O.ai AutoML (wrapped with R) in KNIME for regression problems - a powerful auto-machine-learning framework […]

Iconkn_​automl_​h2o_​regression_​python 

H2O.ai AutoML (wrapped with Python) in KNIME for regression problems - a powerful auto-machine-learning framework […]

Iconkn_​automl_​h2o_​regression 

H2O.ai AutoML (generic KNIME nodes) in KNIME for regression problems - a powerful auto-machine-learning framework […]

Icon04_​train_​model 

01_caption_preprocessing After we cleaned the training captions and pre-calculated image-/word- features, the caption network can be trained. In this […]

IconSunburstChart-Features 

Sunburst Visualization of qualitative features generated with the qualitative annotations plugins in Fiji URL: GitHub repo […]

Iconxgboost parameter tuning (maximise ROC) using Bayes Optimization 

xgboost parameter tuning and handling large datasets This example demonstrates following: 1. Handling Large datasets in KNIME--Setting Memory […]

Iconkn_​example_​python_​iris_​2021 

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 […]

Iconkn_​example_​ml_​multiclass_​wine_​quality 

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