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'.
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model_params |
A list of additional parameters to pass to the model, which can be any parameter defined in the model reference package. Defaults to NULL .
For FNNs, the following elements are defined in the model params list:
input_layer A list defining the input layer. Must include:
units Number of units in the input layer.
activation Activation function for the input layer.
input_shape Input shape for the layer.
layers A list of lists, where each sublist specifies a hidden layer with:
units Number of units in the layer.
activation Activation function for the layer.
output_layer A list defining the output layer. Must include:
units Number of units in the output layer.
activation Activation function for the output layer (e.g., "linear" or "sigmoid" ).
compile_args A list of arguments for compiling the model. Must include:
optimizer Optimizer for training (e.g., "adam" or "sgd" ).
loss Loss function (e.g., "mse" or "binary_crossentropy" ).
metrics Optional list of metrics for evaluation (e.g., c("accuracy") ).
For other learners (e.g., "ridge" or "causal_forest" ), model_params can include relevant hyperparameters.
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