R interfaces to Weka filters.
Normalize(formula, data, subset, na.action, control = NULL)
Discretize(formula, data, subset, na.action, control = NULL)a symbolic description of a model. Note that for unsupervised filters the response can be omitted.
an optional data frame containing the variables in the model.
an optional vector specifying a subset of observations to be used in the fitting process.
a function which indicates what should happen when
the data contain NAs. See model.frame for
details.
an object of class Weka_control, or a
character vector of control options, or NULL (default).
Available options can be obtained on-line using the Weka Option
Wizard WOW, or the Weka documentation.
A data frame.
Normalize implements an unsupervised filter that normalizes all
instances of a dataset to have a given norm. Only numeric values are
considered, and the class attribute is ignored.
Discretize implements a supervised instance filter that
discretizes a range of numeric attributes in the dataset into nominal
attributes. Discretization is by Fayyad & Irani's MDL
method (the default).
Note that these methods ignore nominal attributes, i.e., variables of
class factor.
U. M. Fayyad and K. B. Irani (1993). Multi-interval discretization of continuous-valued attributes for classification learning. Thirteenth International Joint Conference on Artificial Intelligence, 1022--1027. Morgan Kaufmann.
I. H. Witten and E. Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques. 2nd Edition, Morgan Kaufmann, San Francisco.
# NOT RUN {
## Using a Weka data set ...
w <- read.arff(system.file("arff","weather.arff",
package = "RWeka"))
## Normalize (response irrelevant)
m1 <- Normalize(~., data = w)
m1
## Discretize
m2 <- Discretize(play ~., data = w)
m2
# }