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 )
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. |
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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 |
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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 |
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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). |
# NOT RUN { 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) # }