# orange-multitarget / docs / rst / ClusteringRandomForest.rst

 Miran Levar 273d15d 2012-09-11 Aleš Erjavec 4e65e12 2013-09-17 Miran Levar 273d15d 2012-09-11 Aleš Erjavec 4e65e12 2013-09-17  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 ClusteringRandomForest ========================= .. image:: ../../orangecontrib/multitarget/widgets/icons/ClusteringRandomForest.png :alt: Widget icon Signals ------- Inputs: - Data Data to be used for learning. Outputs: - Learner or Classifier Description ----------- .. image:: images/crf1.* :alt: 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 
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