mergeBenchmarkResultTask

[<code><a rd-options="" href="/link/BenchmarkResult?package=mlr&version=2.9" data-mini-rdoc="mlr::BenchmarkResult">BenchmarkResult</a></code>] 
<code>BenchmarkResult</code> objects that should be merged.


Combines the <code><a rd-options="" href="/link/BenchmarkResult?package=mlr&version=2.9" data-mini-rdoc="mlr::BenchmarkResult">BenchmarkResult</a></code> objects that were performed
  on different tasks with the same set of learner(s). 
  This can be helpful if you, e.g. forgot to run the set of learners on a new task


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

mergeBenchmarkResultTask function

[<code><a rd-options='' href='BenchmarkResult'>BenchmarkResult</a></code>] 
<code>BenchmarkResult</code> objects that should be merged.


Combines the <code><a rd-options='' href='BenchmarkResult'>BenchmarkResult</a></code> objects that were performed
  on different tasks with the same set of learner(s). 
  This can be helpful if you, e.g. forgot to run the set of learners on a new task


mergeBenchmarkResultTask: Merge different tasks of BenchmarkResult objects.

Description

Usage

Arguments