summary.posterior

Numerical summaries are created for each model including the
posterior mean and upper and lower credible intervals (aka
uncertainty intervals).

Bayesian analysis used here to answer the question: "when
looking at resampling results, are the differences between models
'real'?" To answer this, a model can be created were the performance
statistic is the resampling statistics (e.g. accuracy or RMSE). These
values are explained by the model types. In doing this, we can get
parameter estimates for each model's affect on performance and make
statistical (and practical) comparisons between models. The methods
included here are similar to Benavoli et al (2017)
<https://jmlr.org/papers/v18/16-305.html>.

Max Kuhn

tidyposterior

Bayesian Analysis to Compare Models using Resampling Statistics

RStudio 

summary.posterior function

<dl><dt>object</dt>
<dd>An object produced by <code>tidy.perf_mod()</code>.</dd>
<dt>prob</dt>
<dd>A number p (0 &lt; p &lt; 1) indicating the desired
probability mass to include in the intervals.</dd>
<dt>seed</dt>
<dd>A single integer for sampling from the posterior.</dd>
<dt>...</dt>
<dd>Not currently used</dd></dl>

Arguments

Summarize the Posterior Distributions of Model Statistics — summary.posterior

<dl>

<dt>object</dt>
<dd>An object produced by <code>tidy.perf_mod()</code>.</dd>


<dt>prob</dt>
<dd>A number p (0 &lt; p &lt; 1) indicating the desired
probability mass to include in the intervals.</dd>


<dt>seed</dt>
<dd>A single integer for sampling from the posterior.</dd>


<dt>...</dt>
<dd>Not currently used</dd>

</dl>

summary.posterior: Summarize the Posterior Distributions of Model Statistics

Description

Usage

Value

Arguments

Examples