sits (version 1.4.0)

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. Cross-validation is a technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform. One round of cross-validation involves partitioning a sample of data into complementary subsets, performing the analysis on one subset (called the training set), and validating the analysis on the other subset (called the validation set or testing set).

The k-fold cross validation method involves splitting the dataset into k-subsets. For each subset is held out while the model is trained on all other subsets. This process is completed until accuracy is determine for each instance in the dataset, and an overall accuracy estimate is provided.

This function returns the confusion matrix, and Kappa values.

Usage

sits_kfold_validate(
  samples,
  folds = 5,
  ml_method = sits_rfor(),
  multicores = 2
)

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.

multicores

Number of cores to process in parallel.

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_ltae <- sits_kfold_validate(
        samples_modis_ndvi,
        folds = 5,
        ml_method = sits_lighttae()
    )
    # use a name
    acc_ltae$name <- "LightTAE"
    # put the result in a list
    results[[length(results) + 1]] <- acc_ltae

    # Machine Learning - Random Forests
    acc_rf <- sits_kfold_validate(
        samples_modis_ndvi,
        folds = 5,
        ml_method = sits_rfor()
    )
    acc_rf$name <- "RandomForests"
    # put the result in a list
    results[[length(results) + 1]] <- acc_rf
    # save to xlsx file
    sits_to_xlsx(
        results,
        file = tempfile("accuracy_mato_grosso_dl_", fileext = ".xlsx")
    )
}