Computes the deviance of the model according to the distribution
family specified in the <code>"family"</code> parameter.

internal

Neural Additive Model framework based on Generalized Additive Models from Hastie & Tibshirani (1990, ISBN:9780412343902), which trains a different neural network to estimate the contribution of each feature to the response variable. The networks are trained independently leveraging the local scoring and backfitting algorithms to ensure that the Generalized Additive Model converges and it is additive. The resultant Neural Network is a highly accurate and interpretable deep learning model, which can be used for high-risk AI practices where decision-making should be based on accountable and interpretable algorithms.

Ines Ortega-Fernandez

neuralGAM

Interpretable Neural Network Based on Generalized Additive
Models

Marta Sestelo

dev function

<dl><dt>muhat</dt>
<dd>current estimation of the response variable</dd>
<dt>y</dt>
<dd>response variable</dd>
<dt>family</dt>
<dd>A description of the link function used in the model:
<code>"gaussian"</code>, <code>"poisson"</code>, or <code>"binomial"</code></dd>
<dt>w</dt>
<dd>weight assigned to each observation. Defaults to 1.</dd></dl>

Arguments

Ines Ortega-Fernandez, Marta Sestelo.

Author

Deviance of the model — dev

<dl>

<dt>muhat</dt>
<dd>current estimation of the response variable</dd>


<dt>y</dt>
<dd>response variable</dd>


<dt>family</dt>
<dd>A description of the link function used in the model:
<code>"gaussian"</code>, <code>"poisson"</code>, or <code>"binomial"</code></dd>


<dt>w</dt>
<dd>weight assigned to each observation. Defaults to 1.</dd>

</dl>

dev: Deviance of the model

Description

Usage

Value

Arguments

Author