Alpha version. Supports only binomial classification problems.
Usage
h2o.psvm(
x,
y,
training_frame,
model_id = NULL,
validation_frame = NULL,
ignore_const_cols = TRUE,
hyper_param = 1,
kernel_type = c("gaussian"),
gamma = -1,
rank_ratio = -1,
positive_weight = 1,
negative_weight = 1,
disable_training_metrics = TRUE,
sv_threshold = 1e-04,
fact_threshold = 1e-05,
feasible_threshold = 0.001,
surrogate_gap_threshold = 0.001,
mu_factor = 10,
max_iterations = 200,
seed = -1
)Arguments
- x
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used.
- y
The name or column index of the response variable in the data. The response must be either a binary categorical/factor variable or a numeric variable with values -1/1 (for compatibility with SVMlight format).
- training_frame
Id of the training data frame.
- model_id
Destination id for this model; auto-generated if not specified.
- validation_frame
Id of the validation data frame.
- ignore_const_cols
Logical. Ignore constant columns. Defaults to TRUE.- hyper_param
Penalty parameter C of the error term Defaults to 1.
- kernel_type
Type of used kernel Must be one of: "gaussian". Defaults to gaussian.
- gamma
Coefficient of the kernel (currently RBF gamma for gaussian kernel, -1 means 1/#features) Defaults to -1.
- rank_ratio
Desired rank of the ICF matrix expressed as an ration of number of input rows (-1 means use sqrt(#rows)). Defaults to -1.
- positive_weight
Weight of positive (+1) class of observations Defaults to 1.
- negative_weight
Weight of positive (-1) class of observations Defaults to 1.
- disable_training_metrics
Logical. Disable calculating training metrics (expensive on large datasets) Defaults to TRUE.- sv_threshold
Threshold for accepting a candidate observation into the set of support vectors Defaults to 0.0001.
- fact_threshold
Convergence threshold of the Incomplete Cholesky Factorization (ICF) Defaults to 1e-05.
- feasible_threshold
Convergence threshold for primal-dual residuals in the IPM iteration Defaults to 0.001.
- surrogate_gap_threshold
Feasibility criterion of the surrogate duality gap (eta) Defaults to 0.001.
- mu_factor
Increasing factor mu Defaults to 10.
- max_iterations
Maximum number of iteration of the algorithm Defaults to 200.
- seed
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number).
Examples
if (FALSE) { # \dontrun{
library(h2o)
h2o.init()
# Import the splice dataset
f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/splice/splice.svm"
splice <- h2o.importFile(f)
# Train the Support Vector Machine model
svm_model <- h2o.psvm(gamma = 0.01, rank_ratio = 0.1,
y = "C1", training_frame = splice,
disable_training_metrics = FALSE)
} # }