predict.svmmaj: Out-of-Sample Prediction from Unseen Data.

In SVMMaj: Implementation of the SVM-Maj Algorithm

Out-of-Sample Prediction from Unseen Data.

Description

This function predicts the predicted value (including intercept), given a previous trained model which has been returned by svmmaj.

Usage

## S3 method for class 'svmmaj'
predict(object, X.new, y = NULL, weights = NULL, show.plot = FALSE, ...)

Arguments

object	Model which has been trained beforehand using svmmaj.
X.new	Attribute matrix of the objects to be predicted, which has the same number of attributes as the untransformed attribute matrix in model.
y	The actual class labels (only if show.plot==TRUE).
weights	The weight of observation as the relative importance of the prediction error of the observation.
show.plot	If show.plot=TRUE, it plots the density of the predicted value for both class labels, if y is not specified, the density of all objects will be plotted.
...	Arguments to be passed to methods.

Value

The predicted value (including intercept) of class q.svmmaj, with attributes:

y	The observed class labels of each object.
yhat	he predicted class labels of each object.
classes	The class labels.

Author(s)

Hok San Yip, Patrick J.F. Groenen, Georgi Nalbantov

References

P.J.F. Groenen, G. Nalbantov and J.C. Bioch (2008) SVM-Maj: a majorization approach to linear support vector machines with different hinge errors.

See Also

svmmaj

Examples

attach(AusCredit)

## model training
model <- svmmaj(X[1:400, ], y[1:400], hinge = 'quadratic', lambda = 1)
## model prediction
q4     <- predict(model, X[-(1:400), ], y[-(1:400)], show.plot = TRUE)
q4

SVMMaj documentation built on Sept. 11, 2024, 6:06 p.m.