Supervised Multivariate


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 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 Main Menu > Learning > Discretization.

  • Not that the Supervised Multivariate discretization algorithm is not available via Node Context Menu > Node Editor > States > Curve > Generate a Discretization.

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