titanic

A data set containing the survival outcome, passenger class, age, sex, and
the number of family members for a large number of passengers aboard the
ill-fated 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 function

A data frame with 1309 observations on the following 6 variables:<ul>
<li><code>survived</code> - binary with levels <code>"yes"</code> for survived and <code>"no"</code>
otherwise;</li>
<li><code>pclass</code> - integer giving the corresponding passenger (i.e., ticket)
class with values 1--3;</li>
<li><code>age</code> - the age in years of the corresponding passenger (with 263
missing values);</li>
<li><code>sex</code> - factor giving the sex of each passenger with levels
<code>"male"</code> and <code>"female"</code>;</li>
<li><code>sibsp</code> - integer giving the number of siblings/spouses aboard for each
passenger (ranges from 0--8);</li>
<li><code>parch</code> - integer giving the number of parents/children aboard for each
passenger (ranges from 0--9).</li>
</ul>

titanic: Survival of Titanic passengers

Description

Arguments

Format