plot.predict_profile

Plot Variable Profile 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) <https://jmlr.org/papers/v19/18-416.html>.

Przemyslaw Biecek

DALEX

moDel Agnostic Language for Exploration and eXplanation

Szymon Maksymiuk

Hubert Baniecki

plot.predict_profile function

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

Arguments

<div class="section" id="ceteris-paribus">
<h3>ceteris_paribus</h3></div><ul>
<li><code>color</code> -- a character. Either name of a color or name of a variable that should be used for coloring</li>
<li><code>size</code> -- a numeric. Size of lines to be plotted</li>
<li><code>alpha</code> -- a numeric between <code>0</code> and <code>1</code>. Opacity of lines</li>
<li><code>facet_ncol</code> -- number of columns for the <code><a href="https://ggplot2.tidyverse.org/reference/facet_wrap.html">facet_wrap</a></code></li>
<li><code>variables</code> -- if not <code>NULL</code> then only <code>variables</code> will be presented</li>
<li><code>variable_type</code> -- a character. If <code>numerical</code> then only numerical variables will be plotted.
 If <code>categorical</code> then only categorical variables will be plotted.</li>
<li><code>title</code> -- a character. Plot title. By default <code>"Ceteris Paribus profile"</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>
<li><code>categorical_type</code> -- a character. How categorical variables shall be plotted? Either <code>"lines"</code> (default) or <code>"bars"</code>.</li>
</ul>

Plot options

Plot Variable Profile Explanations — plot.predict_profile

<dl>

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


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

</dl>

<div class='section' id='ceteris-paribus'>
<h3>ceteris_paribus</h3>

<ul>
<li><code>color</code> -- a character. Either name of a color or name of a variable that should be used for coloring</li>
<li><code>size</code> -- a numeric. Size of lines to be plotted</li>
<li><code>alpha</code> -- a numeric between <code>0</code> and <code>1</code>. Opacity of lines</li>
<li><code>facet_ncol</code> -- number of columns for the <code><a href='https://ggplot2.tidyverse.org/reference/facet_wrap.html'>facet_wrap</a></code></li>
<li><code>variables</code> -- if not <code>NULL</code> then only <code>variables</code> will be presented</li>
<li><code>variable_type</code> -- a character. If <code>numerical</code> then only numerical variables will be plotted.
 If <code>categorical</code> then only categorical variables will be plotted.</li>
<li><code>title</code> -- a character. Plot title. By default <code>"Ceteris Paribus profile"</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>
<li><code>categorical_type</code> -- a character. How categorical variables shall be plotted? Either <code>"lines"</code> (default) or <code>"bars"</code>.</li>
</ul>

</div>

plot.predict_profile: Plot Variable Profile Explanations

Description

Usage

Value

Arguments

Plot options

ceteris_paribus