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
- 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
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
- A data frame.
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.
## 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