summarizeColumns

[<code>data.frame</code> | <code><a rd-options="" href="/link/Task?package=mlr&version=2.9" data-mini-rdoc="mlr::Task">Task</a></code>]
Input data.


Summarizes a data.frame, somewhat differently than the normal <code><a rd-options="" href="/link/summary?package=mlr&version=2.9" data-mini-rdoc="mlr::summary">summary</a></code> function of R.
The function is mainly useful as a basic EDA tool on data.frames before they are converted to tasks,
but can be used on tasks as well.<p></p><p>Columns can be of type numeric, integer, logical, factor, or character.
Characters and logicals will be treated as factors.
</p>

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

summarizeColumns function

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


Summarizes a data.frame, somewhat differently than the normal <code><a rd-options='' href='summary'>summary</a></code> function of R.
The function is mainly useful as a basic EDA tool on data.frames before they are converted to tasks,
but can be used on tasks as well.

Columns can be of type numeric, integer, logical, factor, or character.
Characters and logicals will be treated as factors.


summarizeColumns: Summarize columns of data.frame or task.

Description

Usage

Arguments

Value

See Also

Examples