This chapter provides explanations and examples for the supervised machine learning in the Neo4j Graph Data Science library.
In GDS, our pipelines offer an end-to-end workflow, from feature extraction to training and applying machine learning models. Pipelines can be inspected through the Pipeline catalog. The trained models can then be accessed via the Model catalog and used to make predictions about your graph.
To help with building the ML models, there are additional guides for pre-processing and hyperparameter tuning available in:
The Neo4j GDS library includes the following pipelines to train and apply machine learning models, grouped by quality tier:
Was this page helpful?