Builds an Aggregated Frame of an H2OFrame.
h2o.aggregator( training_frame, x, model_id = NULL, ignore_const_cols = TRUE, target_num_exemplars = 5000, rel_tol_num_exemplars = 0.5, transform = c("NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"), categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), save_mapping_frame = FALSE, num_iteration_without_new_exemplar = 500, export_checkpoints_dir = NULL )
training_frame | Id of the training data frame. |
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x | A vector containing the |
model_id | Destination id for this model; auto-generated if not specified. |
ignore_const_cols |
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target_num_exemplars | Targeted number of exemplars Defaults to 5000. |
rel_tol_num_exemplars | Relative tolerance for number of exemplars (e.g, 0.5 is +/- 50 percents) Defaults to 0.5. |
transform | Transformation of training data Must be one of: "NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE". Defaults to NORMALIZE. |
categorical_encoding | Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO. |
save_mapping_frame |
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num_iteration_without_new_exemplar | The number of iterations to run before aggregator exits if the number of exemplars collected didn't change Defaults to 500. |
export_checkpoints_dir | Automatically export generated models to this directory. |
# NOT RUN { library(h2o) h2o.init() df <- h2o.createFrame(rows = 100, cols = 5, categorical_fraction = 0.6, integer_fraction = 0, binary_fraction = 0, real_range = 100, integer_range = 100, missing_fraction = 0) target_num_exemplars = 1000 rel_tol_num_exemplars = 0.5 encoding = "Eigen" agg <- h2o.aggregator(training_frame = df, target_num_exemplars = target_num_exemplars, rel_tol_num_exemplars = rel_tol_num_exemplars, categorical_encoding = encoding) # }