getConfMatrix

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

pred

[<code>logical(1)</code>]
If <code>TRUE</code> rows are normalized to show relative frequencies.
Default is <code>FALSE</code>.

relative

<code>getConfMatrix</code> is deprecated. Please use <code><a rd-options="" href="/link/calculateConfusionMatrix?package=mlr&version=2.10" data-mini-rdoc="mlr::calculateConfusionMatrix">calculateConfusionMatrix</a></code>. Calculates confusion matrix for (possibly resampled) prediction.
Rows indicate true classes, columns predicted classes. The marginal elements count the number of classification
errors for the respective row or column, i.e., the number of errors
when you condition on the corresponding true (rows) or predicted
(columns) class. The last element in the margin diagonal
displays the total amount of errors. Note that for resampling no further aggregation is currently performed.
All predictions on all test sets are joined to a vector yhat, as are all labels
joined to a vector y. Then yhat is simply tabulated vs y, as if both were computed on
a single test set. This probably mainly makes sense when cross-validation is used for resampling.

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

getConfMatrix function

[<code><a rd-options='' href='Prediction'>Prediction</a></code>]
Prediction object.

getConfMatrix: Confusion matrix.

Description

Usage

Arguments

Value

See Also