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Clustering Random Forest

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Signals

Inputs:
  • Data

    Data to be used for learning.

Outputs:
  • Learner or Classifier

Description

Usage example

A clustering random forest is a random forest consisting of clustering trees. The usage is straightforward and the setting are described below.

Settings

  • Number of trees in forest

    Number of trees in forest.

  • Stop splitting nodes at depth

    Maximal depth of tree.

  • Minimal majority class proportion

    Minimal proportion of the majority class value each of the class variables has to reach to stop induction (only used for classification).

  • Min mean squared error

    Minimal mean squared error each of the class variables has to reach to stop induction (only used for regression).

  • Min. instances in leaves

    Minimal number of instances in leaves. Instance count is weighed.

  • Feature scorer

    • Inter dist (default) - Euclidean distance between centroids of clusters
    • Intra dist - average Euclidean distance of each member of a cluster to the centroid of that cluster
    • Silhouette - silhouette (http://en.wikipedia.org/wiki/Silhouette_(clustering)) measure calculated with euclidean distances between clusters instead of elements of a cluster.
    • Gini-index - calculates the Gini-gain index, should be used with class variables with nominal values