Provided by: libai-decisiontree-perl_0.11-2build1_amd64 
NAME
AI::DecisionTree - Automatically Learns Decision Trees
VERSION
version 0.11
SYNOPSIS
use AI::DecisionTree;
my $dtree = new AI::DecisionTree;
# A set of training data for deciding whether to play tennis
$dtree->add_instance
(attributes => {outlook => 'sunny',
temperature => 'hot',
humidity => 'high'},
result => 'no');
$dtree->add_instance
(attributes => {outlook => 'overcast',
temperature => 'hot',
humidity => 'normal'},
result => 'yes');
... repeat for several more instances, then:
$dtree->train;
# Find results for unseen instances
my $result = $dtree->get_result
(attributes => {outlook => 'sunny',
temperature => 'hot',
humidity => 'normal'});
DESCRIPTION
The "AI::DecisionTree" module automatically creates so-called "decision trees" to explain
a set of training data. A decision tree is a kind of categorizer that use a flowchart-
like process for categorizing new instances. For instance, a learned decision tree might
look like the following, which classifies for the concept "play tennis":
OUTLOOK
/ | \
/ | \
/ | \
sunny/ overcast \rainy
/ | \
HUMIDITY | WIND
/ \ *no* / \
/ \ / \
high/ \normal / \
/ \ strong/ \weak
*no* *yes* / \
*no* *yes*
(This example, and the inspiration for the "AI::DecisionTree" module, come directly from
Tom Mitchell's excellent book "Machine Learning", available from McGraw Hill.)
A decision tree like this one can be learned from training data, and then applied to
previously unseen data to obtain results that are consistent with the training data.
The usual goal of a decision tree is to somehow encapsulate the training data in the
smallest possible tree. This is motivated by an "Occam's Razor" philosophy, in which the
simplest possible explanation for a set of phenomena should be preferred over other
explanations. Also, small trees will make decisions faster than large trees, and they are
much easier for a human to look at and understand. One of the biggest reasons for using a
decision tree instead of many other machine learning techniques is that a decision tree is
a much more scrutable decision maker than, say, a neural network.
The current implementation of this module uses an extremely simple method for creating the
decision tree based on the training instances. It uses an Information Gain metric (based
on expected reduction in entropy) to select the "most informative" attribute at each node
in the tree. This is essentially the ID3 algorithm, developed by J. R. Quinlan in 1986.
The idea is that the attribute with the highest Information Gain will (probably) be the
best attribute to split the tree on at each point if we're interested in making small
trees.
METHODS
Building and Querying the Tree
new(...parameters...)
Creates a new decision tree object and returns it. Accepts the following parameters:
noise_mode
Controls the behavior of the "train()" method when "noisy" data is encountered.
Here "noisy" means that two or more training instances contradict each other, such
that they have identical attributes but different results.
If "noise_mode" is set to "fatal" (the default), the "train()" method will throw
an exception (die). If "noise_mode" is set to "pick_best", the most frequent
result at each noisy node will be selected.
prune
A boolean "prune" parameter which specifies whether the tree should be pruned
after training. This is usually a good idea, so the default is to prune.
Currently we prune using a simple minimum-description-length criterion.
verbose
If set to a true value, some status information will be output while training a
decision tree. Default is false.
purge
If set to a true value, the "do_purge()" method will be invoked during "train()".
The default is true.
max_depth
Controls the maximum depth of the tree that will be created during "train()". The
default is 0, which means that trees of unlimited depth can be constructed.
add_instance(attributes => \%hash, result => $string, name => $string)
Adds a training instance to the set of instances which will be used to form the tree.
An "attributes" parameter specifies a hash of attribute-value pairs for the instance,
and a "result" parameter specifies the result.
An optional "name" parameter lets you give a unique name to each training instance.
This can be used in coordination with the "set_results()" method below.
train()
Builds the decision tree from the list of training instances. If a numeric
"max_depth" parameter is supplied, the maximum tree depth can be controlled (see also
the "new()" method).
get_result(attributes => \%hash)
Returns the most likely result (from the set of all results given to "add_instance()")
for the set of attribute values given. An "attributes" parameter specifies a hash of
attribute-value pairs for the instance. If the decision tree doesn't have enough
information to find a result, it will return "undef".
do_purge()
Purges training instances and their associated information from the DecisionTree
object. This can save memory after training, and since the training instances are
implemented as C structs, this turns the DecisionTree object into a pure-perl data
structure that can be more easily saved with "Storable.pm", for instance.
purge()
Returns true or false depending on the value of the tree's "purge" property. An
optional boolean argument sets the property.
copy_instances(from => $other_tree)
Allows two trees to share the same set of training instances. More commonly, this
lets you train one tree, then re-use its instances in another tree (possibly changing
the instance "result" values using "set_results()"), which is much faster than re-
populating the second tree's instances from scratch.
set_results(\%results)
Given a hash that relates instance names to instance result values, change the result
values as specified.
Tree Introspection
instances()
Returns a reference to an array of the training instances used to build this tree.
nodes()
Returns the number of nodes in the trained decision tree.
depth()
Returns the depth of the tree. This is the maximum number of decisions that would
need to be made to classify an unseen instance, i.e. the length of the longest path
from the tree's root to a leaf. A tree with a single node would have a depth of zero.
rule_tree()
Returns a data structure representing the decision tree. For instance, for the tree
diagram above, the following data structure is returned:
[ 'outlook', {
'rain' => [ 'wind', {
'strong' => 'no',
'weak' => 'yes',
} ],
'sunny' => [ 'humidity', {
'normal' => 'yes',
'high' => 'no',
} ],
'overcast' => 'yes',
} ]
This is slightly remniscent of how XML::Parser returns the parsed XML tree.
Note that while the ordering in the hashes is unpredictable, the nesting is in the
order in which the criteria will be checked at decision-making time.
rule_statements()
Returns a list of strings that describe the tree in rule-form. For instance, for the
tree diagram above, the following list would be returned (though not necessarily in
this order - the order is unpredictable):
if outlook='rain' and wind='strong' -> 'no'
if outlook='rain' and wind='weak' -> 'yes'
if outlook='sunny' and humidity='normal' -> 'yes'
if outlook='sunny' and humidity='high' -> 'no'
if outlook='overcast' -> 'yes'
This can be helpful for scrutinizing the structure of a tree.
Note that while the order of the rules is unpredictable, the order of criteria within
each rule reflects the order in which the criteria will be checked at decision-making
time.
as_graphviz()
Returns a "GraphViz" object representing the tree. Requires that the GraphViz module
is already installed, of course. The object returned will allow you to create PNGs,
GIFs, image maps, or whatever graphical representation of your tree you might want.
A "leaf_colors" argument can specify a fill color for each leaf node in the tree. The
keys of the hash should be the same as the strings appearing as the "result"
parameters given to "add_instance()", and the values should be any GraphViz-style
color specification.
Any additional arguments given to "as_graphviz()" will be passed on to GraphViz's
"new()" method. See the GraphViz docs for more info.
LIMITATIONS
A few limitations exist in the current version. All of them could be removed in future
versions - especially with your help. =)
No continuous attributes
In the current implementation, only discrete-valued attributes are supported. This
means that an attribute like "temperature" can have values like "cool", "medium", and
"hot", but using actual temperatures like 87 or 62.3 is not going to work. This is
because the values would split the data too finely - the tree-building process would
probably think that it could make all its decisions based on the exact temperature
value alone, ignoring all other attributes, because each temperature would have only
been seen once in the training data.
The usual way to deal with this problem is for the tree-building process to figure out
how to place the continuous attribute values into a set of bins (like "cool",
"medium", and "hot") and then build the tree based on these bin values. Future
versions of "AI::DecisionTree" may provide support for this. For now, you have to do
it yourself.
TO DO
All the stuff in the LIMITATIONS section. Also, revisit the pruning algorithm to see how
it can be improved.
AUTHOR
Ken Williams, ken@mathforum.org
SEE ALSO
Mitchell, Tom (1997). Machine Learning. McGraw-Hill. pp 52-80.
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), pp 81-106.
perl, GraphViz