Weka_control: Control Weka Options

Description Usage Arguments Details Value See Also Examples

View source: R/control.R


Set control options for Weka learners.





named arguments of control options, see the details and examples.


The available options for a Weka learner, foo() say, can be queried by WOW(foo) and then conveniently set by Weka_control(). See below for an example.

One can use lists for options taking multiple arguments, see the documentation for SMO for an example.


A list of class Weka_control which can be coerced to character for passing it to Weka.

See Also



## Query J4.8 options:
## Learn J4.8 tree on iris data with default settings:
J48(Species ~ ., data = iris)
## Learn J4.8 tree with reduced error pruning (-R) and 
## minimum number of instances set to 5 (-M 5):
J48(Species ~ ., data = iris, control = Weka_control(R = TRUE, M = 5))

Example output

OpenJDK 64-Bit Server VM warning: Can't detect initial thread stack location - find_vma failed
-U      Use unpruned tree.
-O      Do not collapse tree.
-C <pruning confidence>
        Set confidence threshold for pruning.  (default 0.25)
	Number of arguments: 1.
-M <minimum number of instances>
        Set minimum number of instances per leaf.  (default 2)
	Number of arguments: 1.
-R      Use reduced error pruning.
-N <number of folds>
        Set number of folds for reduced error pruning. One fold is used
        as pruning set.  (default 3)
	Number of arguments: 1.
-B      Use binary splits only.
-S      Do not perform subtree raising.
-L      Do not clean up after the tree has been built.
-A      Laplace smoothing for predicted probabilities.
-J      Do not use MDL correction for info gain on numeric attributes.
-Q <seed>
        Seed for random data shuffling (default 1).
	Number of arguments: 1.
        Do not make split point actual value.
        If set, classifier is run in debug mode and may output
        additional info to the console
        If set, classifier capabilities are not checked before
        classifier is built (use with caution).
        The number of decimal places for the output of numbers in the
        model (default 2).
	Number of arguments: 1.
        The desired batch size for batch prediction (default 100).
	Number of arguments: 1.
J48 pruned tree

Petal.Width <= 0.6: setosa (50.0)
Petal.Width > 0.6
|   Petal.Width <= 1.7
|   |   Petal.Length <= 4.9: versicolor (48.0/1.0)
|   |   Petal.Length > 4.9
|   |   |   Petal.Width <= 1.5: virginica (3.0)
|   |   |   Petal.Width > 1.5: versicolor (3.0/1.0)
|   Petal.Width > 1.7: virginica (46.0/1.0)

Number of Leaves  : 	5

Size of the tree : 	9

J48 pruned tree

Petal.Width <= 0.6: setosa (34.0)
Petal.Width > 0.6
|   Petal.Width <= 1.5: versicolor (32.0/1.0)
|   Petal.Width > 1.5: virginica (34.0/2.0)

Number of Leaves  : 	3

Size of the tree : 	5

Warning message:
system call failed: Cannot allocate memory 

RWeka documentation built on Feb. 3, 2020, 1:10 a.m.