Unsupervised Multivariate

Context

• 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.

Algorithm Details & Recommendations

• 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 Main Menu > Learning > Discretization.

• However, it is not available in the Node Editor (Node Context Menu > Edit > Curve > Generate a Discretization).