reimpute

[<code>data.frame</code> | <code><a rd-options="" href="/link/Task?package=mlr&version=2.10" data-mini-rdoc="mlr::Task">Task</a></code>]
Input data.

[<code>ImputationDesc</code>]
Imputation description as returned by <code><a rd-options="" href="/link/impute?package=mlr&version=2.10" data-mini-rdoc="mlr::impute">impute</a></code>.

desc

This function accepts a data frame or a task and an imputation description
as returned by <code><a rd-options="" href="/link/impute?package=mlr&version=2.10" 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>

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.

Bernd Bischl

Machine Learning in R

reimpute function

[<code>data.frame</code> | <code><a rd-options='' href='Task'>Task</a></code>]
Input data.

[<code>ImputationDesc</code>]
Imputation description as returned by <code><a rd-options='' href='impute'>impute</a></code>.

This function accepts a data frame or a task and an imputation description
as returned by <code><a rd-options='' href='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: Re-impute a data set

Description

Usage

Arguments

Value

See Also