ConfusionMatrix

The result of <code><a rd-options="" href="/link/calculateConfusionMatrix?package=mlr&version=2.10" data-mini-rdoc="mlr::calculateConfusionMatrix">calculateConfusionMatrix</a></code>. Object members:
<dl class="dl-horizontal">
<dt>result [<code>matrix</code>]</dt><dd>Confusion matrix of absolute values and marginals. Can also contain
  row and column sums of observations.</dd>
<dt>task.desc [<code><a rd-options="" href="/link/TaskDesc?package=mlr&version=2.10" data-mini-rdoc="mlr::TaskDesc">TaskDesc</a></code>]</dt><dd>Additional information about the task.</dd>
<dt>sums [<code>logical(1)</code>]</dt><dd>Flag if marginal sums of observations are calculated.</dd>
<dt>relative [<code>logical(1)</code>]</dt><dd>Flag if the relative confusion matrices are calculated.</dd>
<dt>relative.row [<code>matrix</code>]</dt><dd>Confusion matrix of relative values and marginals normalized by row.</dd>
<dt>relative.col [<code>matrix</code>]</dt><dd>Confusion matrix of relative values and marginals normalized by column.</dd>
<dt>relative.error [<code>numeric(1)</code>]</dt><dd>Relative error overall.</dd>
</dl>

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.

ConfusionMatrix: Confusion matrix

Description

Arguments

See Also