validate_params_fnn: Cram Policy: Validate Parameters for Feedforward Neural...

In cramR: Cram Method for Efficient Simultaneous Learning and Evaluation

Cram Policy: Validate Parameters for Feedforward Neural Networks (FNNs)

Description

This function validates user-provided parameters for a Feedforward Neural Network (FNN) model. It ensures the correct structure for input_layer, layers, output_layer, compile_args and fit_params.

Usage

validate_params_fnn(model_type, learner_type, model_params, X)

Arguments

model_type	The model type for policy learning. Options include "causal_forest", "s_learner", and "m_learner". Default is "causal_forest". Note: you can also set model_type to NULL and specify custom_fit and custom_predict to use your custom model.
learner_type	The learner type for the chosen model. Options include "ridge" for Ridge Regression, "fnn" for Feedforward Neural Network and "caret" for Caret. Default is "ridge". if model_type is 'causal_forest', choose NULL, if model_type is 's_learner' or 'm_learner', choose between 'ridge', 'fnn' and 'caret'.
model_params	A named list of parameters provided by the user for configuring the FNN model.
X	A matrix or data frame of covariates for which the parameters are validated.

Value

A named list of validated parameters merged with defaults for any missing values.

cramR documentation built on Aug. 25, 2025, 1:12 a.m.