Feature Importance for Regression Models

[mlauber71]

@Vincentsoy you might want to take a look at the data preparation and regression section of my machine learning collection:

Machine Learning Meta Collection (with KNIME) – mlauber71

You could also take a look at the H2O.ai nodes for regression. Most would also provide a different kind of variable importance:

Unsupervised machine learning (gradient boosting regression)

I set up an example that should look like your data (if not feel free to adapt and inform). We have two years of data (from one city) and data from January and want to predict the coefficient for Feb 2020. [image] 2019 serves as training and 2018 as test data. You might have more complex settings with cross-validation if you want. And also since you have the data you might provide coefficients of some more past months. Each line represents its own time series so to speak. Of course, such a se…

compare several regression models on historical "sales coefficient" – mlauber71

H2O.ai AutoML in KNIME for regression problems

H2O.ai Automl - a powerful auto-machine-learning framework wrapped with KNIME It features various models like Random Forest or XGBoost along with Deep Learning. It has wrappers for R and Python but also could be used from KNIME. The results will be written to a folder and the models will be stored in MOJO format to be used in KNIME (as well as on a Big Data cluster via Sparkling Water). One major parameter to set is the running time the model has to test various models and do some hyperparam…