Description Usage Arguments Value Examples
Train a Support Vector Machine model for classification or regression tasks.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | cuml_svm(
x,
y = NULL,
formula = NULL,
mode = c("classification", "regression"),
cost = 1,
kernel = c("rbf", "tanh", "polynomial", "linear"),
gamma = 1/ncol(x),
coef0 = 0,
degree = 3L,
tol = 0.001,
max_iter = 100L * nrow(x),
nochange_steps = 1000L,
cache_size = 1024,
epsilon = 0.1,
sample_weights = NULL,
cuml_log_level = c("off", "critical", "error", "warn", "info", "debug", "trace")
)
|
x |
The input matrix or dataframe. Each data point should be a row and should consist of numeric values only. |
y |
A numeric vector of desired responses. |
formula |
If 'x' is a dataframe, then a R formula syntax of the form '<response col> ~ .' or '<response col> ~ <predictor 1> + <predictor 2> + ...' may be used to specify the response column and the predictor column(s). |
mode |
Type of task to perform. Should be either "classification" or "regression". |
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. |
A Support Vector Machine classifier / regressor object that can be used with the 'predict' S3 generic to make predictions on new data points.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | library(cuml4r)
model <- cuml_svm(
iris[1:100,],
formula = Species ~ .,
mode = "classification",
kernel = "rbf"
)
predictions <- predict(model, iris[1:100,])
cat("Iris species predictions: ", predictions, "\n")
model <- cuml_svm(
mtcars,
formula = mpg ~ .,
mode = "regression",
kernel = "rbf"
)
predictions <- predict(model, mtcars)
cat("MPG predictions:", predictions, "\n")
|
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