log_loss

internal

Complex machine learning models are often hard to interpret. However, in
many situations it is crucial to understand and explain why a model made a specific
prediction. Shapley values is the only method for such prediction explanation framework
with a solid theoretical foundation. Previously known methods for estimating the Shapley
values do, however, assume feature independence. This package implements methods which accounts for any feature
dependence, and thereby produces more accurate estimates of the true Shapley values.
An accompanying 'Python' wrapper ('pyshapr') is available through PyPI.

Martin Jullum

shapr

Prediction Explanation with Dependence-Aware Shapley Values

Lars Henry Berge Olsen

Annabelle Redelmeier

Jon Lachmann

Nikolai Sellereite

Anders Løland

Jens Christian Wahl

Camilla Lingjærde

Norsk Regnesentral 

log_loss function

<dl><dt>y</dt>
<dd>Numeric vector of true binary responses (values in 0/1).</dd>
<dt>pred</dt>
<dd>Numeric vector (or scalar) of predicted probabilities.</dd></dl>

Arguments

Author

Logistic (Cross-Entropy) Loss — log_loss

<dl>

<dt>y</dt>
<dd>Numeric vector of true binary responses (values in 0/1).</dd>


<dt>pred</dt>
<dd>Numeric vector (or scalar) of predicted probabilities.</dd>

</dl>

Logistic (Cross-Entropy) Loss

log_loss: Logistic (Cross-Entropy) Loss

Description

Usage

Value

Arguments

Author