This function accepts a data frame and a imputation description
as returned by <code><a href="/link/impute?package=mlr&version=2.3" rd-options="" data-mini-rdoc="mlr::impute">impute</a></code> to perform the following actions:
<ol><li>Restore dropped columns, setting them to<code>NA</code></li><li>Add dummy variables for columns as specified in<code>impute</code></li><li>Optionally check factors for new levels to treat them as<code>NA</code>s</li><li>Reorder factor levels to ensure identical integer representation as
    before</li><li>Impute missing values using previously collected data</li></ol>

reimpute

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.

reimpute: Re-impute a data set

Description

Usage

Arguments

Value

See Also