devAskNewPage(ask = FALSE)
# This demo shows different machine learning methods for
# classification of time series
# load the sits library
library(sits)
# load the sits library
library(sits)
if (!requireNamespace("sitsdata", quietly = TRUE)) {
stop("Please install package sitsdata\n",
"Please call devtools::install_github('e-sensing/sitsdata')",
call. = FALSE
)
}
# load the sitsdata library
library(sitsdata)
# load a dataset of time series samples for the Mato Grosso region
data("samples_matogrosso_mod13q1")
# create a list to store the results
results <- list()
# adjust the multicores parameters to suit your machine
## SVM model
print("== Accuracy Assessment = SVM =======================")
acc_svm <- sits_kfold_validate(
samples_matogrosso_mod13q1,
folds = 5,
multicores = 3,
ml_method = sits_svm(kernel = "radial", cost = 10)
)
acc_svm[["name"]] <- "svm_10"
results[[length(results) + 1]] <- acc_svm
# =============== RFOR ==============================
print("== Accuracy Assessment = RFOR =======================")
acc_rfor <- sits_kfold_validate(
samples_matogrosso_mod13q1,
folds = 5,
multicores = 2,
ml_method = sits_rfor()
)
acc_rfor[["name"]] <- "rfor"
results[[length(results) + 1]] <- acc_rfor
# =============== XGBOOST ==============================
# extreme gradient boosting
print("== Accuracy Assessment = XGB =======================")
acc_xgb <- sits_kfold_validate(
samples_matogrosso_mod13q1,
folds = 5,
ml_method = sits_xgboost()
)
acc_xgb[["name"]] <- "xgboost"
results[[length(results) + 1]] <- acc_xgb
sits_to_xlsx(results, file = file.path(tempdir(), "/accuracy_mt_ml.xlsx"))
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