Unsupervised Multivariate

Unsupervised Multivariate is one of the Automatic Discretization algorithms for Continuous variables in Step 4 — Discretization and Aggregation of the Data Import Wizard.
This multivariate discretization method is based on analyzing the relationship between variables.

The Unsupervised Multivariate discretization algorithm focuses on representing multivariate probabilistic dependencies using Random Forests.
Its functionality can be described as follows:
- A new dataset is created as a clone of the original one.
- In this new dataset, each variable is independently shuffled to render all the variables independent while keeping the same statistics for each variable.
- The cloned dataset is concatenated with the original dataset. Then, a target variable is created to differentiate the clone from the original, indicating the independent set versus the original dependent set.
- Various datasets are generated from this concatenated dataset with Data Perturbation.
- For each perturbed dataset, a multivariate tree is learned to predict the target variable with a subset of variables. If a structure is already defined, it is used to bias the selection of the variables for each dataset.
- Extracting the most frequent thresholds produces the discretization.
- Being based on Random Forests, this algorithm is computationally expensive and stochastic by nature, specifically when the number of variables is important.
The Unsupervised Multivariate discretization algorithm is also available after the data import via Menu > Learning > Discretization.
However, it is not available in the Node Editor (Node Context Menu > Edit > Curve > Generate a Discretization).