sits (version 1.12.0)

sits_kfold_validate: Cross-validate temporal patterns

Description

Splits the set of time series into training and validation and perform k-fold cross-validation. Cross-validation is a model validation 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 in practice. 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(data.tb, folds = 5, ml_method = sits_rfor(),
  multicores = 1)

Arguments

data.tb

A sits tibble.

folds

Number of partitions to create.

ml_method

Machine learning method.

multicores

Number of cores for processing.

Value

A tibble containing pairs of reference and predicted values.

Examples

# NOT RUN {
# read a set of samples
data(cerrado_2classes)
# perform a five fold validation with the SVM machine learning method
conf_matrix1.mx <- sits_kfold_validate(cerrado_2classes)
# print the confidence matrix
sits_conf_matrix(conf_matrix1.mx)
# }