sits (version 1.5.3)

sits_kfold_validate: Cross-validate time series samples

Description

Splits the set of time series into training and validation and perform k-fold cross-validation.

Usage

sits_kfold_validate(
  samples,
  folds = 5L,
  ml_method = sits_rfor(),
  filter_fn = NULL,
  impute_fn = impute_linear(),
  multicores = 2L,
  gpu_memory = 4L,
  batch_size = 2L^gpu_memory,
  progress = TRUE
)

Value

A caret::confusionMatrix object to be used for validation assessment.

Arguments

samples

Time series.

folds

Number of partitions to create.

ml_method

Machine learning method.

filter_fn

Smoothing filter to be applied - optional (closure containing object of class "function").

impute_fn

Imputation function to remove NA.

multicores

Number of cores to process in parallel.

gpu_memory

Memory available in GPU in GB (default = 4)

batch_size

Batch size for GPU classification.

progress

Logical: Show progress bar?

Author

Rolf Simoes,

Gilberto Camara,

Examples

if (sits_run_examples()) {
    # A dataset containing a tibble with time series samples
    # for the Mato Grosso state in Brasil
    # create a list to store the results
    results <- list()
    # accuracy assessment lightTAE
    acc_rfor <- sits_kfold_validate(
        samples_modis_ndvi,
        folds = 5,
        ml_method = sits_rfor()
    )
    # use a name
    acc_rfor$name <- "Rfor"
    # put the result in a list
    results[[length(results) + 1]] <- acc_rfor
    # save to xlsx file
    sits_to_xlsx(
        results,
        file = tempfile("accuracy_mato_grosso_dl_", fileext = ".xlsx")
    )
}