BuildSigTree(examples,
nPossibleRes,
ensemble=None,
random=0,
metric=InfoTheory.InfoType.BIASENTROPY,
biasList=[1],
depth=0,
maxDepth=-1,
useCMIM=0,
allowCollections=False,
verbose=0,
**kwargs)
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**Arguments**
- examples: the examples to be classified. Each example
should be a sequence at least three entries long, with
entry 0 being a label, entry 1 a BitVector and entry -1
an activity value
- nPossibleRes: the number of result codes possible
- ensemble: (optional) if this argument is provided, it
should be a sequence which is used to limit the bits
which are actually considered as potential descriptors.
The default is None (use all bits).
- random: (optional) If this argument is nonzero, it
specifies the number of bits to be randomly selected
for consideration at this node (i.e. this toggles the
growth of Random Trees).
The default is 0 (no random descriptor selection)
- metric: (optional) This is an _InfoTheory.InfoType_ and
sets the metric used to rank the bits.
The default is _InfoTheory.InfoType.BIASENTROPY_
- biasList: (optional) If provided, this provides a bias
list for the bit ranker.
See the _InfoTheory.InfoBitRanker_ docs for an explanation
of bias.
The default value is [1], which biases towards actives.
- maxDepth: (optional) the maximum depth to which the tree
will be grown
The default is -1 (no depth limit).
- useCMIM: (optional) if this is >0, the CMIM algorithm
(conditional mutual information maximization) will be
used to select the descriptors used to build the trees.
The value of the variable should be set to the number
of descriptors to be used. This option and the
ensemble option are mutually exclusive (CMIM will not be
used if the ensemble is set), but it happily coexsts
with the random argument (to only consider random subsets
of the top N CMIM bits)
The default is 0 (do not use CMIM)
- depth: (optional) the current depth in the tree
This is used in the recursion and should not be set
by the client.
**Returns**
a SigTree.SigTreeNode with the root of the decision tree
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