sits_lightgbm {sits} | R Documentation |
Train light gradient boosting model
Description
Use LightGBM algorithm to classify samples.
This function is a front-end to the lightgbm
package.
LightGBM (short for Light Gradient Boosting Machine) is a gradient boosting
framework developed by Microsoft that's designed for fast, scalable,
and efficient training of decision tree-based models.
It is widely used in machine learning for classification, regression,
ranking, and other tasks, especially with large-scale data.
Usage
sits_lightgbm(
samples = NULL,
boosting_type = "gbdt",
objective = "multiclass",
min_samples_leaf = 20,
max_depth = 6,
learning_rate = 0.1,
num_iterations = 100,
n_iter_no_change = 10,
validation_split = 0.2,
...
)
Arguments
samples |
Time series with the training samples. |
boosting_type |
Type of boosting algorithm (default = "gbdt") |
objective |
Aim of the classifier (default = "multiclass"). |
min_samples_leaf |
Minimal number of data in one leaf. Can be used to deal with over-fitting. |
max_depth |
Limit the max depth for tree model. |
learning_rate |
Shrinkage rate for leaf-based algorithm. |
num_iterations |
Number of iterations to train the model. |
n_iter_no_change |
Number of iterations without improvements until training stops. |
validation_split |
Fraction of the training data for validation. The model will set apart this fraction and will evaluate the loss and any model metrics on this data at the end of each epoch. |
... |
Other parameters to be passed to 'lightgbm::lightgbm' function. |
Value
Model fitted to input data
(to be passed to sits_classify
).
Author(s)
Gilberto Camara, gilberto.camara@inpe.br
Examples
if (sits_run_examples()) {
# Example of training a model for time series classification
# Retrieve the samples for Mato Grosso
# train a random forest model
lgb_model <- sits_train(samples_modis_ndvi,
ml_method = sits_lightgbm
)
# classify the point
point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI")
# classify the point
point_class <- sits_classify(
data = point_ndvi, ml_model = lgb_model
)
plot(point_class)
}