Train a SVM model.

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

cuda_ml_svm(x, ...)

# S3 method for default
cuda_ml_svm(x, ...)

# S3 method for 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,
  cuML_log_level = c("off", "critical", "error", "warn", "info", "debug", "trace"),
  ...
)

# S3 method for 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,
  cuML_log_level = c("off", "critical", "error", "warn", "info", "debug", "trace"),
  ...
)

# S3 method for 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,
  cuML_log_level = c("off", "critical", "error", "warn", "info", "debug", "trace"),
  ...
)

# S3 method for 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,
  cuML_log_level = c("off", "critical", "error", "warn", "info", "debug", "trace"),
  ...
)

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	Espsilon 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.
cuML_log_level	Log level within cuML library functions. Must be one of "off", "critical", "error", "warn", "info", "debug", "trace". Default: off.
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)

# Classification

model <- cuda_ml_svm(
  formula = Species ~ .,
  data = iris,
  kernel = "rbf"
)

predictions <- predict(model, iris[names(iris) != "Species"])

# Regression

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

predictions <- predict(model, mtcars)