predict_PDP.2D.Plotter

Gives prediction surface for all specified feature pairs in the
 interpreter object (features.2d)

Provides several methods for model distillation and interpretability
for general black box machine learning models and treatment effect estimation
methods. For details on the algorithms implemented, see <https://forestry-labs.github.io/distillML/index.html>
Brian Cho, Theo F. Saarinen, Jasjeet S. Sekhon, Simon Walter.

Theo Saarinen

distillML

Model Distillation and Interpretability Methods for Machine
Learning Models

Brian Cho

Jasjeet Sekhon

Simon Walter

predict_PDP.2D.Plotter function

<dl><dt>object</dt>
<dd>The Interpreter object to use.</dd>
<dt>feat.2d</dt>
<dd>A 2-column dataframe or matrix that gives the first variable in
in the first column, and the second variable in the next. The
number of rows is equal to the number of 2-D PDPs one would like.</dd>
<dt>save</dt>
<dd>A boolean indicator to indicate whether the calculations should be
saved in the interpreter object or not. This can help reduce
computation if the PDP functions are used many times, but requires
additional memory to store the predictions. By default, this is set
to TRUE.</dd></dl>

Arguments

Two Dimensional Prediction Curve for PDP Plots — predict_PDP.2D.Plotter

<dl>

<dt>object</dt>
<dd>The Interpreter object to use.</dd>


<dt>feat.2d</dt>
<dd>A 2-column dataframe or matrix that gives the first variable in
in the first column, and the second variable in the next. The
number of rows is equal to the number of 2-D PDPs one would like.</dd>


<dt>save</dt>
<dd>A boolean indicator to indicate whether the calculations should be
saved in the interpreter object or not. This can help reduce
computation if the PDP functions are used many times, but requires
additional memory to store the predictions. By default, this is set
to TRUE.</dd>

</dl>

predict_PDP.2D.Plotter: Two Dimensional Prediction Curve for PDP Plots

Description

Usage

Value

Arguments

Details