Metadata-Version: 1.1
Name: DecisionTree
Version: 3.2.1
Summary: A Python module for decision-tree based classification of multidimensional data
Home-page: https://engineering.purdue.edu/kak/distDT/DecisionTree-3.2.1.html
Author: Avinash Kak
Author-email: kak@purdue.edu
License: Python Software Foundation License
Download-URL: https://engineering.purdue.edu/kak/distDT/DecisionTree-3.2.1.tar.gz
Description:  
        
        **Version 3.2.1** has a bugfix that was needed in one of the probability calculating functions.
        
        **Version 3.2.0** adds boosting capability to the decision tree module.
        
        **Version 3.0** adds bagging capability to the decision tree module.  If you have a large enough training dataset, you can now construct multiple decision trees and have the final classification be based on a majority vote from all the trees.  This can average out the noise in the classification process.
        
        **Version 2.3** gives the module a new capability ---
        ability to introspect about the classification decisions at
        the nodes of the decision tree.
        
        With regard to the purpose of the module, assuming you have
        placed your training data in a CSV file, all you have to do
        is to supply the name of the file to this module and it does
        the rest for you without much effort on your part for
        classifying a new data sample.  A decision tree classifier
        consists of feature tests that are arranged in the form of a
        tree. The feature test associated with the root node is one
        that can be expected to maximally disambiguate the different
        possible class labels for a new data record.  From the root
        node hangs a child node for each possible outcome of the
        feature test at the root. This maximal class-label
        disambiguation rule is applied at the child nodes
        recursively until you reach the leaf nodes.  A leaf node may
        correspond either to the maximum depth desired for the
        decision tree or to the case when there is nothing further
        to gain by a feature test at the node.
        
        Typical usage syntax:
        
        ::
        
              training_datafile = "stage3cancer.csv"
              dt = DecisionTree.DecisionTree( 
                              training_datafile = training_datafile,
                              csv_class_column_index = 2,
                              csv_columns_for_features = [3,4,5,6,7,8],
                              entropy_threshold = 0.01,
                              max_depth_desired = 8,
                              symbolic_to_numeric_cardinality_threshold = 10,
                   )
        
                dt.get_training_data()
                dt.calculate_first_order_probabilities()
                dt.calculate_class_priors()
                dt.show_training_data()
                root_node = dt.construct_decision_tree_classifier()
                root_node.display_decision_tree("   ")
        
                test_sample  = ['g2 = 4.2',
                                'grade = 2.3',
                                'gleason = 4',
                                'eet = 1.7',
                                'age = 55.0',
                                'ploidy = diploid']
                classification = dt.classify(root_node, test_sample)
                print "Classification: ", classification
        
                  
Keywords: data classification,decision trees,information analysis
Platform: All platforms
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
