Weka_classifiers_functions {RWeka} | R Documentation |
R interfaces to Weka regression and classification function learners.
LinearRegression(formula, data, subset, na.action, control = NULL) Logistic(formula, data, subset, na.action, control = NULL) SMO(formula, data, subset, na.action, control = 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. |
control |
a character vector with control options, or NULL
(default). Available options can be obtained on-line using the Weka
Option Wizard WOW , or the Weka documentation. |
There is a predict
method for
predicting from the fitted models.
LinearRegression
builds suitable linear regression models,
using the Akaike criterion for model selection.
Logistic
builds multinomial logistic regression models based on
ridge estimation (le Cessie and van Houwelingen, 1992).
SMO
implements John C. Platt's sequential minimal optimization
algorithm for training a support vector classifier using polynomial or
RBF kernels. Multi-class problems are solved using pairwise
classification.
The model formulae should only use + to indicate the variables to be included.
A list inheriting from classes Weka_functions
and
Weka_classifiers
with components including
classifier |
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. |
predictions |
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). |
call |
the matched call. |
J. C. Platt (1998). Fast training of Support Vector Machines using Sequential Minimal Optimization. In B. Schoelkopf, C. Burges, and A. Smola (eds.), Advances in Kernel Methods — Support Vector Learning. MIT Press.
I. H. Witten and Eibe Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques. 2nd Edition, Morgan Kaufmann, San Francisco.