titanic_mice

The titanic data set contains 263 missing values (i.e., <code>NA</code>'s) in the
<code>age</code> column. This version of the data contains imputed values for the
<code>age</code> column using multivariate imputation by chained equations via
the <a href="https://cran.r-project.org/package=mice">mice</a> package. Consequently,
this is a list containing 11 imputed versions of the observations contained
in the titanic data frame; each completed data set has the same dimension
and column structure as titanic.

datasets

A general framework for constructing variable importance plots from
various types of machine learning models in R. Aside from some standard model-
specific variable importance measures, this package also provides model-
agnostic approaches that can be applied to any supervised learning algorithm.
These include 1) an efficient permutation-based variable importance measure,
2) variable importance based on Shapley values (Strumbelj and Kononenko,
2014) <doi:10.1007/s10115-013-0679-x>, and 3) the variance-based
approach described in Greenwell et al. (2018) <doi:10.48550/arXiv.1805.04755>. A
variance-based method for quantifying the relative strength of interaction
effects is also included (see the previous reference for details).

Brandon Greenwell

Variable Importance Plots

Brad Boehmke

titanic_mice function

The titanic data set contains 263 missing values (i.e., <code>NA</code>'s) in the
<code>age</code> column. This version of the data contains imputed values for the
<code>age</code> column using multivariate imputation by chained equations via
the <a href='https://cran.r-project.org/package=mice'>mice</a> package. Consequently,
this is a list containing 11 imputed versions of the observations contained
in the titanic data frame; each completed data set has the same dimension
and column structure as titanic.

titanic_mice: Survival of Titanic passengers

Description

Arguments