train_classifier: Train a random forest or SVM classifier
In azvoleff/teamlucc: TEAM land use and cover change data processing toolkit

Description Usage Arguments Details Value Examples

This function trains a Support Vector Machine (SVM) or Random Forest (RF) classifier for use in an image classification.

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train_classifier(train_data, type = "rf", use_training_flag = TRUE,
  train_control = NULL, tune_grid = NULL, use_rfe = FALSE,
  factors = list(), ...)

`train_data`	a `link{pixel_data}` object
`type`	either "svm" (to fit a support vector machine) or "rf" (to fit a random forest).
`use_training_flag`	indicates whether to exclude data flagged as testing data when training the classifier. For this to work the input train_data `data.frame` must have a column named 'training_flag' that indicates, for each pixel, whether that pixel is a training pixel (coded as TRUE) or testing pixel (coded as FALSE).
`train_control`	default is NULL (reasonable values will be set automatically). For details see `trainControl`.
`tune_grid`	the training grid to be used for training the classifier. See Details.
`use_rfe`	whether to use Recursive Feature Extraction (RFE) as implemented in the `caret` package to select a subset of the input features to be used in the classification. NOT YET SUPPORTED.
`factors`	a list of character vector giving the names of predictors (layer names from the images used to build `train_data`) that should be treated as factors, and specifying the levels of each factor. For example, `factors=list(year=c(1990, 1995, 2000, 2005, 2010))`.
`...`	additional arguments (such as `ntree` for random forest classifier) to pass to `train`

For type='svm', tunegrid must be a data.frame with two columns: ".sigma" and ".C". For type='rf', must be a data.frame with one column: '.mtry'.

This function will run in parallel if a parallel backend is registered with foreach.

a trained model (as a train object from the caret package)

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train_data <- get_pixels(L5TSR_1986, L5TSR_1986_2001_training, "class_1986", 
                         training=.6)
model <- train_classifier(train_data)