plot.model_parts

an object of the class <code>model_parts</code>

Plot Variable Importance Explanations

Unverified black box model is the path to the 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 of direct interpretability.
DALEX package contains various methods that help to understand the link between input variables
and model output. Implemented methods help to explore 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

<h3>variable_importance</h3><ul>
<li>max_vars maximal number of features to be included in the plot. default value is <code>10</code></li>
<li>show_boxplots logical if <code>TRUE</code> (default) boxplot will be plotted to show permutation data.</li>
<li>bar_width width of bars. By default <code>10</code></li>
<li>desc_sorting logical. Should the bars be sorted descending? By default <code>TRUE</code></li>
<li>title the plot's title, by default <code>'Feature Importance'</code></li>
<li>subtitle 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.model_parts: Plot Variable Importance Explanations

Description

Usage

Arguments

Value

Plot options

variable_importance