Alpha version. Supports only binomial classification problems.
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)(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.
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model.
Id of the training data frame.
Destination id for this model; auto-generated if not specified.
Id of the validation data frame.
Logical. Ignore constant columns. Defaults to TRUE.
Penalty parameter C of the error term Defaults to 1.
Type of used kernel Must be one of: "gaussian". Defaults to gaussian.
Coefficient of the kernel (currently RBF gamma for gaussian kernel, -1 means 1/#features) Defaults to -1.
Desired rank of the ICF matrix expressed as an ration of number of input rows (-1 means use sqrt(#rows)). Defaults to -1.
Weight of positive (+1) class of observations Defaults to 1.
Weight of positive (-1) class of observations Defaults to 1.
Logical. Disable calculating training metrics (expensive on large datasets) Defaults to TRUE.
Threshold for accepting a candidate observation into the set of support vectors Defaults to 0.0001.
Convergence threshold of the Incomplete Cholesky Factorization (ICF) Defaults to 1e-05.
Convergence threshold for primal-dual residuals in the IPM iteration Defaults to 0.001.
Feasibility criterion of the surrogate duality gap (eta) Defaults to 0.001.
Increasing factor mu Defaults to 10.
Maximum number of iteration of the algorithm Defaults to 200.
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).