makeImputeMethod

[<code>function(data, target, col, ...)</code>]
Function to learn and extract information on column <code>col</code>
out of data frame <code>data</code>. Argument <code>target</code> specifies
the target column of the learning task.
The function has to return a named list of values.

learn

[<code>function(data, target, col, ...)</code>]
Function to impute missing values in <code>col</code> using information
returned by <code>learn</code> on the same column.
All list elements of the return values o <code>learn</code>
are passed to this function into <code>...</code>.

impute

[<code>list</code>]
Named list of arguments to pass to <code>learn</code> via <code>...</code>.

args

This is a constructor to create your own imputation methods.

Interface to a large number of classification and regression
techniques, including machine-readable parameter descriptions. There is
also an experimental extension for survival analysis, clustering and
general, example-specific cost-sensitive learning. Generic resampling,
including cross-validation, bootstrapping and subsampling. Hyperparameter
tuning with modern optimization techniques, for single- and multi-objective
problems. Filter and wrapper methods for feature selection. Extension of
basic learners with additional operations common in machine learning, also
allowing for easy nested resampling. Most operations can be parallelized.

makeImputeMethod: Create a custom imputation method.

Description

Usage

Arguments

See Also