embed_dataset | | Reduce the dataset to an n-dimensional numeric vector embedding |
embed_images | | Embed images using pretrained DL models |
embed_items | | Trains an item2vec model on provided lists of items (or sentences of words, etc.) |
embed_sessions | | Trains an item2vec model on provided lists of items |
embed_text | | Parse and calculate a (word-averaged) embedding vector for each text |
embed_text_with_model | | Use language models to calulate an embedding for each text in provided column |
embed_with_trees | | Reduce the dataset to an n-dimensional numeric vector embedding using a Forest model’s tree indices |
layout_dataset | | Reduce the dataset to 2 dimensions that can be mapped to x/y node positions |
vectorize_dataset | | Create a vectorized (numeric) dataset, (optionally) of reduced dimensionality |