Package
weka.classifiers.trees
Synopsis
A Hoeffding tree (VFDT) is an incremental, anytime decision tree induction algorithm that is capable of learning from massive data streams, assuming that the distribution generating examples does not change over time. Hoeffding trees exploit the fact that a small sample can often be enough to choose an optimal splitting attribute. This idea is supported mathematically by the Hoeffding bound, which quantifies the number of observations (in our case, examples) needed to estimate some statistics within a prescribed precision (in our case, the goodness of an attribute).
A theoretically appealing feature of Hoeffding Trees not shared by otherincremental decision tree learners is that it has sound guarantees of performance. Using the Hoeffding bound one can show that its output is asymptotically nearly identical to that of a non-incremental learner using infinitely many examples. For more information see:
Geoff Hulten, Laurie Spencer, Pedro Domingos: Mining time-changing data streams. In: ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 97-106, 2001.
This classifier will be available in Weka >= 3.7.10.
Options
The table below describes the options available for HoeffdingTree.
Option |
Description |
|---|---|
debug |
If set to true, classifier may output additional info to the console. |
gracePeriod |
Number of instances (or total weight of instances) a leaf should observe between split attempts. |
hoeffdingTieThreshold |
Theshold below which a split will be forced to break ties. |
leafPredictionStrategy |
The leaf prediction strategy to use |
minimumFractionOfWeightInfoGain |
Minimum fraction of weight required down at least two branches for info gain splitting. |
naiveBayesPredictionThreshold |
The number of instances (weight) a leaf should observe before allowing naive Bayes (adaptive) to make predictions |
printLeafModels |
Print leaf models (naive bayes leaves only) |
splitConfidence |
The allowable error in a split decision. Values closer to zero will take longer to decide. |
splitCriterion |
The splitting criterion to use |
Capabilities
The table below describes the capabilities of HoeffdingTree.
Capability |
Supported |
|---|---|
Class |
Missing class values, Nominal class, Binary class |
Attributes |
Nominal attributes, Numeric attributes, Unary attributes, Date attributes, Empty nominal attributes, Missing values, Binary attributes |
Min # of instances |
0 |