.mc_dropout_forward

Run <code>passes</code> stochastic forward passes with Dropout active at prediction time.
Each pass samples a dropout mask and produces predictions, simulating epistemic
uncertainty.

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

.mc_dropout_forward function

<dl><dt>model</dt>
<dd>Fitted Keras model for one smooth term.</dd>
<dt>x</dt>
<dd>Input matrix (converted to TensorFlow tensor internally).</dd>
<dt>passes</dt>
<dd>Number of stochastic passes (&gt;=2).</dd>
<dt>output_dim</dt>
<dd>Expected number of outputs per observation
(e.g., 1 = mean only, 3 = quantile heads (lwr, upr, mean)).</dd></dl>

Arguments

Ines Ortega-Fernandez, Marta Sestelo

Author

Internal helper: MC Dropout forward sampling — .mc_dropout_forward

<dl>

<dt>model</dt>
<dd>Fitted Keras model for one smooth term.</dd>


<dt>x</dt>
<dd>Input matrix (converted to TensorFlow tensor internally).</dd>


<dt>passes</dt>
<dd>Number of stochastic passes (&gt;=2).</dd>


<dt>output_dim</dt>
<dd>Expected number of outputs per observation
(e.g., 1 = mean only, 3 = quantile heads (lwr, upr, mean)).</dd>

</dl>

.mc_dropout_forward: Internal helper: MC Dropout forward sampling

Description

Usage

Value

Arguments

Author