Make Predictions from a

Similar to other predict methods, this function predicts fitted values from a fitted "RDTRegressor" object.

# S3 method for RDTRegressor
predict(
  model,
  data,
  key,
  features = NULL,
  block.size = NULL,
  missing.replacement = NULL
)

Arguments

model	`R6Class object` A "RDTRegressor" object for prediction.
data	`DataFrame` DataFrame containting the data.
key	`character` Name of the ID column.
features	`character of list of characters, optional` Name of feature columns for prediction. If not provided, it defaults to all non-key columns of data.
block.size	`integer, optional` The number of rows loaded per time during prediction. 0 indicates load all data at once. Defaults to 0.
missing.replacement	`character, optional` The missing replacement strategy: "feature.marginalized": marginalize each missing feature out independently. "instance.marginalized": marginalize all missing features in an instance as a whole corresponding to each category. Defaults to "feature.marginalized".

S3 methods

DataFrame of predicted values, structured as follows.

ID, with same name and type as data's ID column.
SCORE, type NVARCHAR, predicted values.
CONFIDENCE, type DOUBLE. Representing the confidence of a class. All 0s for regression.

Call the function with a "RDTRegressor" object rfr:

> predict(rfr, data, key = "ID")