Ignore:
Timestamp:
03/15/12 10:53:52 (2 years ago)
Author:
blaz <blaz.zupan@…>
Branch:
default
Message:

Changed wording in RST documentation.

File:
1 edited

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  • Orange/ensemble/forest.py

    r10525 r10530  
    5353class RandomForestLearner(Orange.core.Learner): 
    5454    """ 
    55     Just like in bagging, classifiers in random forests are trained from bootstrap 
    56     samples of training data. Here, the classifiers are trees. However, to increase 
    57     randomness, at each node of the tree the best feature is 
    58     chosen from a subset of features in the data. We closely follow the 
    59     original algorithm (Brieman, 2001) both in implementation and parameter 
     55    Trains an ensemble predictor consisting of trees trained 
     56    on bootstrap 
     57    samples of training data. To increase 
     58    randomness, the tree learner considers only a subset of 
     59    candidate features at each node. The algorithm closely follows 
     60    the original procedure (Brieman, 2001) both in implementation and parameter 
    6061    defaults. 
    6162         
     
    6465 
    6566    :param attributes: number of randomly drawn features among 
    66             which to select the best to split the nodes in tree 
    67             induction. The default, None, means the square root of 
     67            which to select the best one to split the data sets 
     68            in tree nodes. The default, None, means the square root of 
    6869            the number of features in the training data. Ignored if 
    6970            :obj:`learner` is specified. 
     
    8990 
    9091    :param callback: a function to be called after every iteration of 
    91             induction of classifier. This is called with parameter  
    92             (from 0.0 to 1.0) that gives estimates on learning progress. 
     92            induction of classifier. The call includes a parameter 
     93            (from 0.0 to 1.0) that provides an estimate 
     94            of completion of the learning progress. 
    9395 
    9496    :param name: name of the learner. 
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