plot.model_parts

Plot Variable Importance Explanations

Any unverified black box model is the path to failure. Opaqueness leads to distrust.
Distrust leads to ignoration. Ignoration leads to rejection.
DALEX package xrays any model and helps to explore and explain its behaviour.
Machine Learning (ML) models are widely used and have various applications in classification
or regression. Models created with boosting, bagging, stacking or similar techniques are often
used due to their high performance. But such black-box models usually lack direct interpretability.
DALEX package contains various methods that help to understand the link between input variables
and model output. Implemented methods help to explore the model on the level of a single instance
as well as a level of the whole dataset.
All model explainers are model agnostic and can be compared across different models.
DALEX package is the cornerstone for 'DrWhy.AI' universe of packages for visual model exploration.
Find more details in (Biecek 2018) <arXiv:1806.08915>.

Przemyslaw Biecek

DALEX

moDel Agnostic Language for Exploration and eXplanation

Szymon Maksymiuk

Hubert Baniecki

plot.model_parts function

<dl><dt>x</dt>
<dd>an object of the class <code>model_parts</code></dd>
<dt>...</dt>
<dd>other parameters described below</dd></dl>

Arguments

<div class="section" id="variable-importance">
<h3>variable_importance</h3></div><ul>
<li><code>max_vars</code> maximal number of features to be included in the plot. default value is <code>10</code></li>
<li><code>show_boxplots</code> logical if <code>TRUE</code> (default) boxplot will be plotted to show permutation data.</li>
<li><code>bar_width</code> width of bars. By default <code>10</code></li>
<li><code>desc_sorting</code> logical. Should the bars be sorted descending? By default <code>TRUE</code></li>
<li><code>title</code> the plot's title, by default <code>'Feature Importance'</code></li>
<li><code>subtitle</code> a character. Plot subtitle. By default <code>NULL</code> - then subtitle is set to "created for the XXX, YYY model",
 where XXX, YYY are labels of given explainers.</li>
</ul>

Plot options

Plot Variable Importance Explanations — plot.model_parts

<dl>

<dt>x</dt>
<dd>an object of the class <code>model_parts</code></dd>


<dt>...</dt>
<dd>other parameters described below</dd>

</dl>

<div class='section' id='variable-importance'>
<h3>variable_importance</h3>

<ul>
<li><code>max_vars</code> maximal number of features to be included in the plot. default value is <code>10</code></li>
<li><code>show_boxplots</code> logical if <code>TRUE</code> (default) boxplot will be plotted to show permutation data.</li>
<li><code>bar_width</code> width of bars. By default <code>10</code></li>
<li><code>desc_sorting</code> logical. Should the bars be sorted descending? By default <code>TRUE</code></li>
<li><code>title</code> the plot's title, by default <code>'Feature Importance'</code></li>
<li><code>subtitle</code> a character. Plot subtitle. By default <code>NULL</code> - then subtitle is set to "created for the XXX, YYY model",
 where XXX, YYY are labels of given explainers.</li>
</ul>

</div>

plot.model_parts: Plot Variable Importance Explanations

Description

Usage

Value

Arguments

Plot options

variable_importance