extends Orange by providing methods that allow for classification of datasets
-Currently supported techniques: clustering trees, PLS, binary relevance, chain classification and neural networks (for multi-label data only).
+Currently supported techniques:
+ * Partial Least Squares
.. _Orange: http://orange.biolab.si/
will be found at:
+Documentation d at:
:obj:`ClusteringTreeLearner` is an implementation of classification and regression
trees, based on the :obj:`SimpleTreeLearner`. It is implemented in C++ for speed and low memory usage.
-Features are selected by finding the furthest apart clusters measured with the euclidean distance between prototypes,
-which are the means of clusters.
+Clustering trees work by splitting the data into clusters based on attributes. The attribute provides the optimal split based on a measure,
+the default used in this implementation is the Euclidean distance between the centroids of clusters, which we try to maximize.
:obj:`ClusteringTreeLearner` was developed for speeding up the construction
of random forests, but can also be used as a standalone tree learner.