Cram Policy: Validate Parameters for Feedforward Neural Networks (FNNs)
Source:R/validate_params_fnn.R
validate_params_fnn.RdThis 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.
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.