cuda_ml_svm: Train a SVM model.

In cuda.ml: R Interface for the RAPIDS cuML Suite of Libraries

Train a SVM model.

Description

Train a Support Vector Machine model for classification or regression tasks.

Usage

cuda_ml_svm(x, ...)

## Default S3 method:
cuda_ml_svm(x, ...)

## S3 method for class 'data.frame'
cuda_ml_svm(
  x,
  y,
  cost = 1,
  kernel = c("rbf", "tanh", "polynomial", "linear"),
  gamma = NULL,
  coef0 = 0,
  degree = 3L,
  tol = 0.001,
  max_iter = NULL,
  nochange_steps = 1000L,
  cache_size = 1024,
  epsilon = 0.1,
  sample_weights = NULL,
  ...
)

## S3 method for class 'matrix'
cuda_ml_svm(
  x,
  y,
  cost = 1,
  kernel = c("rbf", "tanh", "polynomial", "linear"),
  gamma = NULL,
  coef0 = 0,
  degree = 3L,
  tol = 0.001,
  max_iter = NULL,
  nochange_steps = 1000L,
  cache_size = 1024,
  epsilon = 0.1,
  sample_weights = NULL,
  ...
)

## S3 method for class 'formula'
cuda_ml_svm(
  formula,
  data,
  cost = 1,
  kernel = c("rbf", "tanh", "polynomial", "linear"),
  gamma = NULL,
  coef0 = 0,
  degree = 3L,
  tol = 0.001,
  max_iter = NULL,
  nochange_steps = 1000L,
  cache_size = 1024,
  epsilon = 0.1,
  sample_weights = NULL,
  ...
)

## S3 method for class 'recipe'
cuda_ml_svm(
  x,
  data,
  cost = 1,
  kernel = c("rbf", "tanh", "polynomial", "linear"),
  gamma = NULL,
  coef0 = 0,
  degree = 3L,
  tol = 0.001,
  max_iter = NULL,
  nochange_steps = 1000L,
  cache_size = 1024,
  epsilon = 0.1,
  sample_weights = NULL,
  ...
)

Arguments

x	Depending on the context: A data frame of predictors. A matrix of predictors. A recipe specifying a set of preprocessing steps created from recipes::recipe(). A formula specifying the predictors and the outcome.
...	Optional arguments; currently unused.
y	A numeric vector (for regression) or factor (for classification) of desired responses.
cost	A positive number for the cost of predicting a sample within or on the wrong side of the margin. Default: 1.
kernel	Type of the SVM kernel function (must be one of "rbf", "tanh", "polynomial", or "linear"). Default: "rbf".
gamma	The gamma coefficient (only relevant to polynomial, RBF, and tanh kernel functions, see explanations below). Default: 1 / (num features). The following kernels are implemented: RBF K(x_1, x_2) = exp(-gamma |x_1-x_2|^2) TANH K(x_1, x_2) = tanh(gamma <x_1,x_2> + coef0) POLYNOMIAL K(x_1, x_2) = (gamma <x_1,x_2> + coef0)^degree LINEAR K(x_1,x_2) = <x_1,x_2>, where < , > denotes the dot product.
coef0	The 0th coefficient (only applicable to polynomial and tanh kernel functions, see explanations below). Default: 0. The following kernels are implemented: RBF K(x_1, x_2) = exp(-gamma |x_1-x_2|^2) TANH K(x_1, x_2) = tanh(gamma <x_1,x_2> + coef0) POLYNOMIAL K(x_1, x_2) = (gamma <x_1,x_2> + coef0)^degree LINEAR K(x_1,x_2) = <x_1,x_2>, where < , > denotes the dot product.
degree	Degree of the polynomial kernel function (note: not applicable to other kernel types, see explanations below). Default: 3. The following kernels are implemented: RBF K(x_1, x_2) = exp(-gamma |x_1-x_2|^2) TANH K(x_1, x_2) = tanh(gamma <x_1,x_2> + coef0) POLYNOMIAL K(x_1, x_2) = (gamma <x_1,x_2> + coef0)^degree LINEAR K(x_1,x_2) = <x_1,x_2>, where < , > denotes the dot product.
tol	Tolerance to stop fitting. Default: 1e-3.
max_iter	Maximum number of outer iterations in SmoSolver. Default: 100 * (num samples).
nochange_steps	Number of steps with no change w.r.t convergence. Default: 1000.
cache_size	Size of kernel cache (MiB) in device memory. Default: 1024.
epsilon	Epsilon parameter of the epsilon-SVR model. There is no penalty for points that are predicted within the epsilon-tube around the target values. Please note this parameter is only relevant for regression tasks. Default: 0.1.
sample_weights	Optional weight assigned to each input data point.
formula	A formula specifying the outcome terms on the left-hand side, and the predictor terms on the right-hand side.
data	When a recipe or formula is used, data is specified as a data frame containing the predictors and (if applicable) the outcome.

Value

A SVM classifier / regressor object that can be used with the 'predict' S3 generic to make predictions on new data points.

Examples

library(cuda.ml)

if (interactive() && cuda_ml_backend_info()$runtime_installed) {
  # Classification

  two_class <- modeldata::two_class_dat

  model <- cuda_ml_svm(
    formula = Class ~ .,
    data = two_class,
    kernel = "rbf"
  )

  predictors <- subset(two_class, select = -Class)
  predictions <- predict(model, predictors)

  # Regression

  model <- cuda_ml_svm(
    formula = mpg ~ .,
    data = mtcars,
    kernel = "rbf"
  )

  predictions <- predict(model, mtcars)
}

cuda.ml documentation built on Aug. 21, 2026, 9:14 a.m.