Package rdkit :: Package ML :: Package DecTree :: Module ID3
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Module ID3

source code

ID3 Decision Trees

contains an implementation of the ID3 decision tree algorithm
as described in Tom Mitchell's book "Machine Learning"

It relies upon the _Tree.TreeNode_ data structure (or something
  with the same API) defined locally to represent the trees 

Functions [hide private]
 
CalcTotalEntropy(examples, nPossibleVals)
Calculates the total entropy of the data set (w.r.t.
source code
 
GenVarTable(examples, nPossibleVals, vars)
Generates a list of variable tables for the examples passed in.
source code
 
ID3(examples, target, attrs, nPossibleVals, depth=0, maxDepth=-1, **kwargs)
Implements the ID3 algorithm for constructing decision trees.
source code
 
ID3Boot(examples, attrs, nPossibleVals, initialVar=None, depth=0, maxDepth=-1, **kwargs)
Bootstrapping code for the ID3 algorithm
source code
Variables [hide private]
  __package__ = 'rdkit.ML.DecTree'

Imports: numpy, DecTree, entropy, range, xrange


Function Details [hide private]

CalcTotalEntropy(examples, nPossibleVals)

source code 
Calculates the total entropy of the data set (w.r.t. the results)

**Arguments** 

 - examples: a list (nInstances long) of lists of variable values + instance
           values
 - nPossibleVals: a list (nVars long) of the number of possible values each variable
   can adopt.

**Returns**

  a float containing the informational entropy of the data set.
 

GenVarTable(examples, nPossibleVals, vars)

source code 
Generates a list of variable tables for the examples passed in.

  The table for a given variable records the number of times each possible value
  of that variable appears for each possible result of the function.

**Arguments**

  - examples: a list (nInstances long) of lists of variable values + instance
            values

  - nPossibleVals: a list containing the number of possible values of
                 each variable + the number of values of the function.

  - vars:  a list of the variables to include in the var table


**Returns**

    a list of variable result tables. Each table is a Numeric array
      which is varValues x nResults

ID3(examples, target, attrs, nPossibleVals, depth=0, maxDepth=-1, **kwargs)

source code 
Implements the ID3 algorithm for constructing decision trees.

From Mitchell's book, page 56

This is *slightly* modified from Mitchell's book because it supports
  multivalued (non-binary) results.

**Arguments**

  - examples: a list (nInstances long) of lists of variable values + instance
          values

  - target: an int

  - attrs: a list of ints indicating which variables can be used in the tree

  - nPossibleVals: a list containing the number of possible values of
               every variable.

  - depth: (optional) the current depth in the tree

  - maxDepth: (optional) the maximum depth to which the tree
               will be grown

**Returns**

 a DecTree.DecTreeNode with the decision tree

**NOTE:** This code cannot bootstrap (start from nothing...)
      use _ID3Boot_ (below) for that.

ID3Boot(examples, attrs, nPossibleVals, initialVar=None, depth=0, maxDepth=-1, **kwargs)

source code 
Bootstrapping code for the ID3 algorithm

see ID3 for descriptions of the arguments

If _initialVar_ is not set, the algorithm will automatically
 choose the first variable in the tree (the standard greedy
 approach).  Otherwise, _initialVar_ will be used as the first
 split.