Supervised Multivariate

Context

• Supervised Multivariate is one of the Automatic Discretization algorithms for Continuous variables in Step 4 — Discretization and Aggregation of the Data Import Wizard.

Algorithm Details & Recommendations

• The Supervised Multivariate discretization algorithm focuses on representing the multivariate probabilistic dependencies involving a Target variable.
• It utilizes Random Forests to find the most useful thresholds for predicting the Target variable.
• Its function can be summarized as follows:
  • Data Perturbation (opens in a new tab) generates a range of datasets.
  • 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 final discretization.
• The Supervised Multivariate takes into account the Minimum Interval Weight and can improve the generalization capability of the model.
• Being based on Random Forests, this algorithm is computationally expensive and stochastic by nature.
• After the conclusion of the Data Import Wizard, the Supervised Multivariate discretization algorithm is also available from Menu > Learning > Discretization.
• Not that the Supervised Multivariate discretization algorithm is not available via Node Context Menu > Node Editor > States > Curve > Generate a Discretization.