# Weka_filters

##### R/Weka Filters

R interfaces to Weka filters.

##### Usage

```
Normalize(formula, data, subset, na.action, control = NULL)
Discretize(formula, data, subset, na.action, control = NULL)
```

##### Arguments

- formula
- a symbolic description of a model. Note that for unsupervised filters the response can be omitted.
- 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. - control
- 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

##### Details

`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`

.

##### Value

- A data frame.

##### References

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.

##### Examples

```
## 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
```

*Documentation reproduced from package RWeka, version 0.4-3, License: GPL-2*