predict.svm {e1071} | R Documentation |
This function predicts values based upon a model trained by svm
.
predict(object, newdata, ...)
object |
object of class "svm" , created by svm . |
newdata |
a matrix containing the new input data. |
... |
currently not used. |
The predicted value (for classification: the label, for density
estimation: TRUE
or FALSE
).
David Meyer (based on C++-code by Chih-Chung Chang and Chih-Jen Lin)
david.meyer@ci.tuwien.ac.at
data(iris) attach(iris) ## classification mode # default with factor response: model <- svm (Species~., data=iris) # alternatively the traditional interface: x <- subset (iris, select = -Species) y <- Species model <- svm (x, y) print (model) summary (model) # test with train data pred <- predict (model, x) # Check accuracy: table (pred,y) ## try regression mode on two dimensions # create data x <- seq (0.1,5,by=0.05) y <- log(x) + rnorm (x, sd=0.2) # estimate model and predict input values m <- svm (x,y) new <- predict (m,x) # visualize plot (x,y) points (x, log(x), col=2) points (x, new, col=4) ## density-estimation # create 2-dim. normal with rho=0: X <- data.frame (a=rnorm (1000), b=rnorm (1000)) attach (X) # traditional way: m <- svm (X) # formula interface: m <- svm (~a+b) # or: m <- svm (~., data=X) # visualization: plot (X) points (X[m$index,], col=2)