{ "info": { "author": "Avinash Kak", "author_email": "kak@purdue.edu", "bugtrack_url": null, "classifiers": [ "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.4", "Topic :: Scientific/Engineering :: Information Analysis" ], "description": "Consult the module API page at \n\n https://engineering.purdue.edu/kak/distDT/DecisionTree-3.4.3.html\n\nfor all information related to this module, including\ninformation regarding the latest changes to the code. The\npage at the URL shown above lists all of the module\nfunctionality you can invoke in your own code. That page\nalso describes in great detail how you can use the boosting\nand the bagging capabilities of the module, and the\ncapabilities allowed by the new RandomizedTreesForBigData\nclass that was introduced in Version 3.3.0. Recent changes\nto the module allow you to tackle needle-in-a-haystack and\nbig-data classification problems. The needle-in-a-haystack\nmetaphor is useful when your training data is excessively\ndominated by just one class.\n\nWith regard to the basic purpose of the module, assuming you\nhave placed your training data in a CSV file, all you have\nto do is to supply the name of the file to this module and\nit does the rest for you without much effort on your part\nfor classifying a new data sample. A decision tree\nclassifier consists of feature tests that are arranged in\nthe form of a tree. The feature test associated with the\nroot node is one that can be expected to maximally\ndisambiguate the different possible class labels for a new\ndata record. From the root node hangs a child node for each\npossible outcome of the feature test at the root. This\nmaximal class-label disambiguation rule is applied at the\nchild nodes recursively until you reach the leaf nodes. A\nleaf node may correspond either to the maximum depth desired\nfor the decision tree or to the case when there is nothing\nfurther to gain by a feature test at the node.\n\nTypical usage syntax:\n\n::\n\n training_datafile = \"stage3cancer.csv\"\n dt = DecisionTree.DecisionTree( \n training_datafile = training_datafile,\n csv_class_column_index = 2,\n csv_columns_for_features = [3,4,5,6,7,8],\n entropy_threshold = 0.01,\n max_depth_desired = 8,\n symbolic_to_numeric_cardinality_threshold = 10,\n )\n\n dt.get_training_data()\n dt.calculate_first_order_probabilities()\n dt.calculate_class_priors()\n dt.show_training_data()\n root_node = dt.construct_decision_tree_classifier()\n root_node.display_decision_tree(\" \")\n\n test_sample = ['g2 = 4.2',\n 'grade = 2.3',\n 'gleason = 4',\n 'eet = 1.7',\n 'age = 55.0',\n 'ploidy = diploid']\n classification = dt.classify(root_node, test_sample)\n print \"Classification: \", classification", "description_content_type": null, "docs_url": null, 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