summary.missingness

Interpreting <code>missingness</code> results from wide
 datasets is difficult. This function helps interpret missingness output by
 summarizing this output by listing: the percent of variables that
 contain missingness, the variable name of the variable with the maximum
 amount of missingness along with its percent of observations containing
 missing values, and a tibble that lists the top 5 missingness levels with
 the count of the number of variables associated with each level (0
 missingness level is ignored). If there are no variables with missingness,
 a message that reports no missingness is printed and NULL is returned
 instead.

Aims to make machine learning in healthcare as easy as possible.
You can develop customized, reliable, high-performance machine learning models with minimal code.
Models are created with automatic preprocessing, hyperparameter tuning, and
algorithm selection (between
'xgboost' Chen, T. & Guestrin, C. (2016) <arXiv:1603.02754>,
'ranger' Wright, M. N., & Ziegler, A. (2017) <doi:10.18637/jss.v077.i01>,
and 'glm' Friedman J, Hastie T, Tibshirani R. (2010) <doi:10.18637/jss.v033.i01>)
so that they can be easily
put into production. Additionally, there are tools to help understand how a model makes its
predictions, select prediction threshholds for operational use, and evaluate model performance over time.
Code uses 'tidyverse' syntax and most methods have an associated visualization.

Mike Mastanduno

healthcareai

Tools for Healthcare Machine Learning

Levi Thatcher

Michael Levy

Taylor Larsen

Taylor Miller

Rex Sumsion

summary.missingness function

<dl><dt>object</dt>
<dd>Data frame from <code>missingness</code></dd>
<dt>...</dt>
<dd>Unused</dd></dl>

Arguments

Summarizes data given by missingness — summary.missingness

<dl>

<dt>object</dt>
<dd>Data frame from <code>missingness</code></dd>


<dt>...</dt>
<dd>Unused</dd>

</dl>

summary.missingness: Summarizes data given by `missingness`

Description

Usage

Value

Arguments

Examples