The TwoStep node provides a form of cluster analysis. It can be used to cluster the dataset into distinct groups when you don’t know what those groups are at the beginning.
- For more information about this node, see TwoStep Overview.
- For more information about the visualizations for this node, see TwoStep Visualizations.
As with Kohonen nodes and K-Means nodes, TwoStep models do not use a target field (and are thus forms of unsupervised learning). Instead of trying to predict an outcome, TwoStep tries to uncover patterns in the set of input fields. Records are grouped so that records within a group or cluster tend to be similar to each other, but records in different groups are dissimilar.
Like your visualization? Why not deploy it? For more information, see Deploy a model.