This function performs batch-wise learning for machine learning models.
Usage
ml_learning(
data,
formula = NULL,
batch,
parallelize_batch = FALSE,
loss_name = NULL,
caret_params = NULL,
custom_fit = NULL,
custom_predict = NULL,
custom_loss = NULL,
n_cores = detectCores() - 1,
classify = FALSE
)Arguments
- data
A matrix or data frame of features. Must include the target variable.
- formula
Formula specifying the relationship between the target and predictors for supervised learning.
- batch
Either an integer specifying the number of batches (randomly sampled) or a vector of length equal to the sample size indicating batch assignment for each observation.
- parallelize_batch
Logical. Whether to parallelize batch processing. Defaults to `FALSE`.
- loss_name
The name of the loss function to be used (e.g., `"se"`, `"logloss"`).
- caret_params
A list of parameters to pass to the `caret::train()` function. - Required: `method` (e.g., `"glm"`, `"rf"`).
- custom_fit
A custom function for training user-defined models. Defaults to `NULL`.
- custom_predict
A custom function for making predictions from user-defined models. Defaults to `NULL`.
- custom_loss
Optional custom function for computing the loss of a trained model on the data. Should return a vector containing per-instance losses.
- n_cores
Number of CPU cores to use for parallel processing (`parallelize_batch = TRUE`). Defaults to `detectCores() - 1`.
- classify
Indicate if this is a classification problem. Defaults to FALSE