KNIME WEKA nodes (3.7) version 4.1.0.v201911281246 by KNIME AG, Zurich, Switzerland
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.
(based on WEKA 3.7)
For further options, click the 'More' - button in the dialog.
All weka dialogs have a panel where you can specify classifier-specific parameters.
L: The leaf prediction strategy to use. 0 = majority class, 1 = naive Bayes, 2 = naive Bayes adaptive. (default = 2)
S: The splitting criterion to use. 0 = Gini, 1 = Info gain (default = 1)
E: The allowable error in a split decision - values closer to zero will take longer to decide (default = 1e-7)
H: Threshold below which a split will be forced to break ties (default = 0.05)
M: Minimum fraction of weight required down at least two branches for info gain splitting (default = 0.01)
G: Grace period - the number of instances a leaf should observe between split attempts (default = 200)
N: The number of instances (weight) a leaf should observe before allowing naive Bayes to make predictions (NB or NB adaptive only) (default = 0)
P: Print leaf models when using naive Bayes at the leaves.
The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.
Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.
Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, Missing values, Nominal class, Binary class, Missing class values] Dependencies:  min # Instance: 0
It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.
To use this node in KNIME, install KNIME Weka Data Mining Integration (3.7) from the following update site:
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