| Step | Fast | Description |
|---|---|---|
| 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 |
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