Perform inplace imputation by filling missing values with aggregates computed on the "na.rm'd" vector. Additionally, it's possible to perform imputation based on groupings of columns from within data; these columns can be passed by index or name to the by parameter. If a factor column is supplied, then the method must be "mode".
h2o.impute( data, column = 0, method = c("mean", "median", "mode"), combine_method = c("interpolate", "average", "lo", "hi"), by = NULL, groupByFrame = NULL, values = NULL )
| data | The dataset containing the column to impute. |
|---|---|
| column | A specific column to impute, default of 0 means impute the whole frame. |
| method | "mean" replaces NAs with the column mean; "median" replaces NAs with the column median; "mode" replaces with the most common factor (for factor columns only); |
| combine_method | If method is "median", then choose how to combine quantiles on even sample sizes. This parameter is ignored in all other cases. |
| by | group by columns |
| groupByFrame | Impute the column col with this pre-computed grouped frame. |
| values | A vector of impute values (one per column). NaN indicates to skip the column |
an H2OFrame with imputed values
The default method is selected based on the type of the column to impute. If the column is numeric then "mean" is selected; if it is categorical, then "mode" is selected. Other column types (e.g. String, Time, UUID) are not supported.
if (FALSE) { h2o.init() iris_hf <- as.h2o(iris) iris_hf[sample(nrow(iris_hf), 40), 5] <- NA # randomly replace 50 values with NA # impute with a group by iris_hf <- h2o.impute(iris_hf, "Species", "mode", by = c("Sepal.Length", "Sepal.Width")) }