LinearRegression(formula, data, subset, na.action, control = Weka_control())
Logistic(formula, data, subset, na.action, control = Weka_control())
SMO(formula, data, subset, na.action, control = Weka_control())
NA
s.Weka_control
.
Available options can be obtained on-line using the Weka
Option Wizard WOW
, or the Weka documentation.Weka_functions
and
Weka_classifiers
with components includingjobjRef
) to a Java object
obtained by applying the Weka buildClassifier
method to build
the specified model using the given control options.classifyInstance
method for the built classifier and
each instance).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.
I. H. Witten and Eibe Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques. 2nd Edition, Morgan Kaufmann, San Francisco.