svm {e1071} | R Documentation |
svm
is used to train a support vector machine. It can be used to carry
out general regression and classification (of nu and epsilon-type), as
well as density-estimation. A formula interface is provided.
## S3 method for class 'formula': svm(formula, data = NULL, ..., subset, na.action = na.omit, scale = TRUE) ## Default S3 method: svm(x, y = NULL, scale = TRUE, type = NULL, kernel = "radial", degree = 3, gamma = 1 / ncol(as.matrix(x)), coef0 = 0, cost = 1, nu = 0.5, class.weights = NULL, cachesize = 40, tolerance = 0.001, epsilon = 0.1, shrinking = TRUE, cross = 0, probability = FALSE, fitted = TRUE, ..., subset, na.action = na.omit)
formula |
a symbolic description of the model to be fit. |
data |
an optional data frame containing the variables in the model. By default the variables are taken from the environment which ‘svm’ is called from. |
x |
a data matrix, a vector, or a sparse matrix (object of class
matrix.csr as provided by the package SparseM). |
y |
a response vector with one label for each row/component of x . Can be either
a factor (for classification tasks) or a numeric vector (for
regression). |
scale |
A logical vector indicating the variables to be
scaled. If scale is of length 1, the value is recycled as
many times as needed.
Per default, data are scaled internally (both x and y
variables) to zero mean and unit variance. The center and scale
values are returned and used for later predictions. |
type |
svm can be used as a classification
machine, as a regresson machine, or for novelty detection.
Depending of whether y is
a factor or not, the default setting for type is C-classification or eps-regression , respectively, but may be overwritten by setting an explicit value.Valid options are:
|
kernel |
the kernel used in training and predicting. You
might consider changing some of the following parameters, depending
on the kernel type.
|
degree |
parameter needed for kernel of type polynomial (default: 3) |
gamma |
parameter needed for all kernels except linear
(default: 1/(data dimension)) |
coef0 |
parameter needed for kernels of type polynomial
and sigmoid (default: 0) |
cost |
cost of constraints violation (default: 1)—it is the ‘C’-constant of the regularization term in the Lagrange formulation. |
nu |
parameter needed for nu-classification ,
nu-regression , and one-classification |
class.weights |
a named vector of weights for the different classes, used for asymetric class sizes. Not all factor levels have to be supplied (default weight: 1). All components have to be named. |
cachesize |
cache memory in MB (default 40) |
tolerance |
tolerance of termination criterion (default: 0.001) |
epsilon |
epsilon in the insensitive-loss function (default: 0.1) |
shrinking |
option whether to use the shrinking-heuristics
(default: TRUE ) |
cross |
if a integer value k>0 is specified, a k-fold cross validation on the training data is performed to assess the quality of the model: the accuracy rate for classification and the Mean Squared Error for regression |
fitted |
logical indicating whether the fitted values should be computed
and included in the model or not (default: TRUE ) |
probability |
logical indicating whether the model should allow for probability predictions. |
... |
additional parameters for the low level fitting function
svm.default |
subset |
An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.) |
na.action |
A function to specify the action to be taken if NA s are
found. The default action is na.omit , which leads to rejection of cases
with missing values on any required variable. An alternative
is na.fail , which causes an error if NA cases
are found. (NOTE: If given, this argument must be named.) |
For multiclass-classification with k levels, k>2, libsvm
uses the
‘one-against-one’-approach, in which k(k-1)/2 binary classifiers are
trained; the appropriate class is found by a voting scheme.
libsvm
internally uses a sparse data representation, which is
also high-level supported by the package SparseM.
If the predictor variables include factors, the formula interface must be used to get a correct model matrix.
plot.svm
allows a simple graphical
visualization of classification models.
The probability model for classification fits a logistic distribution using maximum likelihood to the decision values of all binary classifiers, and computes the a-posteriori class probabilities for the multi-class problem using quadratic optimization. The probabilistic regression model assumes (zero-mean) laplace-distributed errors for the predictions, and estimates the scale parameter using maximum likelihood.
An object of class "svm"
containing the fitted model, including:
SV |
The resulting support vectors (possibly scaled). |
index |
The index of the resulting support vectors in the data
matrix. Note that this index refers to the preprocessed data (after
the possible effect of na.omit and subset ) |
coefs |
The corresponding coefficients times the training labels. |
rho |
The negative intercept. |
sigma |
In case of a probabilistic regression model, the scale parameter of the hypothesized (zero-mean) laplace distribution estimated by maximum likelihood. |
probA, probB |
numeric vectors of length k(k-1)/2, k number of classes, containing the parameters of the logistic distributions fitted to the decision values of the binary classifers (1 / (1 + exp(a x + b))). |
Data are scaled internally, usually yielding better results.
Parameters of SVM-models usually must be tuned to yield sensible results!
David Meyer (based on C/C++-code by Chih-Chung Chang and Chih-Jen Lin)
David.Meyer@R-project.org
predict.svm
plot.svm
tune.svm
matrix.csr
(in package SparseM)
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) # (same as:) pred <- fitted(model) # Check accuracy: table(pred, y) # compute decision values and probabilities: pred <- predict(model, x, decision.values = TRUE) attr(pred, "decision.values")[1:4,] # visualize (classes by color, SV by crosses): plot(cmdscale(dist(iris[,-5])), col = as.integer(iris[,5]), pch = c("o","+")[1:150 %in% model$index + 1]) ## try regression mode on two dimensions # create data x <- seq(0.1, 5, by = 0.05) y <- log(x) + rnorm(x, sd = 0.2) # estimate model and predict input values m <- svm(x, y) new <- predict(m, x) # visualize plot(x, y) points(x, log(x), col = 2) points(x, new, col = 4) ## density-estimation # create 2-dim. normal with rho=0: X <- data.frame(a = rnorm(1000), b = rnorm(1000)) attach(X) # traditional way: m <- svm(X, gamma = 0.1) # formula interface: m <- svm(~., data = X, gamma = 0.1) # or: m <- svm(~ a + b, gamma = 0.1) # test: newdata <- data.frame(a = c(0, 4), b = c(0, 4)) predict (m, newdata) # visualize: plot(X, col = 1:1000 %in% m$index + 1, xlim = c(-5,5), ylim=c(-5,5)) points(newdata, pch = "+", col = 2, cex = 5) # weights: (example not particularly sensible) i2 <- iris levels(i2$Species)[3] <- "versicolor" summary(i2$Species) wts <- 100 / table(i2$Species) wts m <- svm(Species ~ ., data = i2, class.weights = wts)