R interfaces to Weka lazy learners.

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

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**.

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.

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.

D. Aha and D. Kibler (1991).
Instance-based learning algorithms.
*Machine Learning*, **6**, 37--66.
10.1007/BF00153759.

Z. Zheng and G. Webb (2000).
Lazy learning of Bayesian rules.
*Machine Learning*, **41**/1, 53--84.
10.1023/A:1007613203719.