R interfaces to Weka classifiers.

Supervised learners, i.e., algorithms for classification and regression, are termed “classifiers” by Weka. (Numeric prediction, i.e., regression, is interpreted as prediction of a continuous class.)

R interface functions to Weka classifiers are created by
`make_Weka_classifier`

, and have formals `formula`

,
`data`

, `subset`

, `na.action`

, and `control`

(default: none), where the first four have the “usual” meanings
for statistical modeling functions in R, and the last again specifies
the control options to be employed by the Weka learner.

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

See `model.frame`

for details on how `na.action`

is
used.

Objects created by these interfaces always inherit from class
`Weka_classifier`

, and have at least suitable `print`

,
`summary`

(via `evaluate_Weka_classifier`

), and
`predict`

methods.

Available “standard” interface functions are documented in Weka_classifier_functions (regression and classification function learners), Weka_classifier_lazy (lazy learners), Weka_classifier_meta (meta learners), Weka_classifier_rules (rule learners), and Weka_classifier_trees (regression and classification tree learners).