# Weka_classifier_lazy

##### R/Weka Lazy Learners

R interfaces to Weka lazy learners.

- Keywords
- models, regression, classif

##### Usage

```
IBk(formula, data, subset, na.action,
control = Weka_control(), options = NULL)
LBR(formula, data, subset, na.action,
control = Weka_control(), options = NULL)
```

##### Arguments

- formula
a symbolic description of the model to be fit.

- data
an optional data frame containing the variables in the model.

- subset
an optional vector specifying a subset of observations to be used in the fitting process.

- na.action
a function which indicates what should happen when the data contain

`NA`

s. See`model.frame`

for details.- control
an object of class

`Weka_control`

giving options to be passed to the Weka learner. Available options can be obtained on-line using the Weka Option Wizard`WOW`

, or the Weka documentation.- options
a named list of further options, or

`NULL`

(default). See**Details**.

##### Details

There are a `predict`

method for
predicting from the fitted models, and a `summary`

method based
on `evaluate_Weka_classifier`

.

`IBk`

provides a \(k\)-nearest neighbors classifier, see Aha &
Kibler (1991).

`LBR`

(“Lazy Bayesian Rules”) implements a lazy learning
approach to lessening the attribute-independence assumption of naive
Bayes as suggested by Zheng & Webb (2000).

The model formulae should only use the `+` and `-` operators
to indicate the variables to be included or not used, respectively.

Argument `options`

allows further customization. Currently,
options `model`

and `instances`

(or partial matches for
these) are used: if set to `TRUE`

, the model frame or the
corresponding Weka instances, respectively, are included in the fitted
model object, possibly speeding up subsequent computations on the
object. By default, neither is included.

##### Value

A list inheriting from classes `Weka_lazy`

and
`Weka_classifiers`

with components including

a reference (of class
`jobjRef`

) to a Java object
obtained by applying the Weka `buildClassifier`

method to build
the specified model using the given control options.

a numeric vector or factor with the model
predictions for the training instances (the results of calling the
Weka `classifyInstance`

method for the built classifier and
each instance).

the matched call.

##### Note

`LBR`

requires Weka package lazyBayesianRules to be
installed.

##### References

D. Aha and D. Kibler (1991).
Instance-based learning algorithms.
*Machine Learning*, **6**, 37--66.

Z. Zheng and G. Webb (2000).
Lazy learning of Bayesian rules.
*Machine Learning*, **41**/1, 53--84.

##### See Also

*Documentation reproduced from package RWeka, version 0.4-40, License: GPL-2*