Description Usage Arguments Value Author(s)
Use generalized liner model (glm) via penalized maximim likelihood to classify data. This function is a front-end to the "cv.glmnet" method in the "glmnet" package. Please refer to the documentation in that package for more details.
1 2 3 | sits_gbm(distances.tb = NULL, formula = sits_formula_logref(),
distribution = "multinomial", n.trees = 5000, interaction.depth = 4,
shrinkage = 0.001, cv.folds = 5, n.cores = 1, ...)
|
distances.tb |
a time series with a set of distance measures for each training sample |
formula |
a symbolic description of the model to be fit. SITS offers a set of such formulas (default: sits_formula_logref) |
distribution |
the name of the distribution. Either "multinomial" for classification) |
n.trees |
Number of trees to fit. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times. (default: 500) |
interaction.depth |
The maximum depth of variable interactions. 1 implies an additive model, 2 implies a model with up to 2-way interactions. |
shrinkage |
a shrinkage parameter applied to each tree in the expansion. Also known as the learning rate or step-size reduction. |
cv.folds |
number of cross-validations to run |
n.cores |
number of cores to run |
... |
other parameters to be passed to 'gbm::gbm' function |
result either an model function to be passed in sits_predict or an function prepared that can be called further to compute multinom training model
Alexandre Xavier Ywata de Carvalho, alexandre.ywata@ipea.gov.br
Rolf Simoes, rolf.simoes@inpe.br
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