h2o.prcomp(training_frame, x, model_id = NULL, validation_frame = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, transform = c("NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"), pca_method = c("GramSVD", "Power", "Randomized", "GLRM"), k = 1, max_iterations = 1000, use_all_factor_levels = FALSE, compute_metrics = TRUE, impute_missing = FALSE, seed = -1, max_runtime_secs = 0)
character
names of the predictors in the model.Logical
. Ignore constant columns. Defaults to TRUE.Logical
. Whether to score during each iteration of model training. Defaults to FALSE.Logical
. Whether first factor level is included in each categorical expansion Defaults to FALSE.Logical
. Whether to compute metrics on the training data Defaults to TRUE.Logical
. Whether to impute missing entries with the column mean Defaults to FALSE.h2o.svd
, h2o.glrm
library(h2o)
h2o.init()
ausPath <- system.file("extdata", "australia.csv", package="h2o")
australia.hex <- h2o.uploadFile(path = ausPath)
h2o.prcomp(training_frame = australia.hex, k = 8, transform = "STANDARDIZE")
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