select_sample

Function <code>select_sample</code> selects subset of rows from data set.
This is useful if data is large and we need just a sample to calculate profiles.

Collection of tools for assessment of feature importance and feature effects.
Key functions are:
feature_importance() for assessment of global level feature importance,
ceteris_paribus() for calculation of the what-if plots,
partial_dependence() for partial dependence plots,
conditional_dependence() for conditional dependence plots,
accumulated_dependence() for accumulated local effects plots,
aggregate_profiles() and cluster_profiles() for aggregation of ceteris paribus profiles,
generic print() and plot() for better usability of selected explainers,
generic plotD3() for interactive, D3 based explanations, and
generic describe() for explanations in natural language.
The package 'ingredients' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.

Przemyslaw Biecek

ingredients

Effects and Importances of Model Ingredients

Hubert Baniecki

Adam Izdebski

select_sample function

<dl><dt>data</dt>
<dd>set of observations. Profile will be calculated for every observation (every row)</dd>
<dt>n</dt>
<dd>number of observations to select.</dd>
<dt>seed</dt>
<dd>seed for random number generator.</dd></dl>

Arguments

Select Subset of Rows — select_sample

<dl>

<dt>data</dt>
<dd>set of observations. Profile will be calculated for every observation (every row)</dd>


<dt>n</dt>
<dd>number of observations to select.</dd>


<dt>seed</dt>
<dd>seed for random number generator.</dd>

</dl>

select_sample: Select Subset of Rows

Description

Usage

Value

Arguments

Details

Examples