Creates a data frame in H2O with real-valued, categorical, integer, and binary columns specified by the user.
h2o.createFrame( rows = 10000, cols = 10, randomize = TRUE, value = 0, real_range = 100, categorical_fraction = 0.2, factors = 100, integer_fraction = 0.2, integer_range = 100, binary_fraction = 0.1, binary_ones_fraction = 0.02, time_fraction = 0, string_fraction = 0, missing_fraction = 0.01, response_factors = 2, has_response = FALSE, seed, seed_for_column_types )
rows | The number of rows of data to generate. |
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cols | The number of columns of data to generate. Excludes the response column if |
randomize | A logical value indicating whether data values should be randomly generated. This must be TRUE if either |
value | If |
real_range | The range of randomly generated real values. |
categorical_fraction | The fraction of total columns that are categorical. |
factors | The number of (unique) factor levels in each categorical column. |
integer_fraction | The fraction of total columns that are integer-valued. |
integer_range | The range of randomly generated integer values. |
binary_fraction | The fraction of total columns that are binary-valued. |
binary_ones_fraction | The fraction of values in a binary column that are set to 1. |
time_fraction | The fraction of randomly created date/time columns. |
string_fraction | The fraction of randomly created string columns. |
missing_fraction | The fraction of total entries in the data frame that are set to NA. |
response_factors | If |
has_response | A logical value indicating whether an additional response column should be pre-pended to the final H2O data frame. If set to TRUE, the total number of columns will be |
seed | A seed used to generate random values when |
seed_for_column_types | A seed used to generate random column types when |
Returns an H2OFrame object.
# NOT RUN { library(h2o) h2o.init() hf <- h2o.createFrame(rows = 1000, cols = 100, categorical_fraction = 0.1, factors = 5, integer_fraction = 0.5, integer_range = 1, has_response = TRUE) head(hf) summary(hf) hf <- h2o.createFrame(rows = 100, cols = 10, randomize = FALSE, value = 5, categorical_fraction = 0, integer_fraction = 0) summary(hf) # }