Description
The SVMSparsePredict (td_svm_sparse_predict_mle
) function takes the
model output by the function SVMSparse (td_svm_sparse_mle
) and a set
of test samples (in sparse format) and outputs a prediction for each sample.
Note: This function is only available when tdplyr is connected to Vantage 1.1
or later versions.
Usage
td_svm_sparse_predict_mle ( object = NULL, newdata = NULL, sample.id.column = NULL, attribute.column = NULL, value.column = NULL, accumulate.label = NULL, output.class.num = NULL, output.response.probdist = TRUE, output.responses = NULL, newdata.sequence.column = NULL, object.sequence.column = NULL, newdata.partition.column = NULL, newdata.order.column = NULL, object.order.column = NULL )
Arguments
object |
Required Argument. |
object.order.column |
Optional Argument. |
newdata |
Required Argument. |
newdata.partition.column |
Required Argument. |
newdata.order.column |
Optional Argument. |
sample.id.column |
Required Argument. |
attribute.column |
Required Argument. |
value.column |
Optional Argument. |
accumulate.label |
Optional Argument. |
output.class.num |
Optional Argument.
Types: numeric |
output.response.probdist |
Optional Argument. |
output.responses |
Optional Argument.
Types: character OR vector of characters |
newdata.sequence.column |
Optional Argument. |
object.sequence.column |
Optional Argument. |
Value
Function returns an object of class "td_svm_sparse_predict_mle" which
is a named list containing Teradata tbl object.
Named list member can be referenced directly with the "$" operator
using name: result.
Examples
# Get the current context/connection con <- td_get_context()$connection # Load example data. loadExampleData("svmsparsepredict_example", "svm_iris_input_test", "svm_iris_input_train") # Create remote tibble objects. svm_iris_input_train <- tbl(con, "svm_iris_input_train") svm_iris_input_test <- tbl(con, "svm_iris_input_test") # Example 1 - # Generate SparseSVMTrainer model based on train data "svm_iris_input_train". svm_iris_model <- td_svm_sparse_mle(data = svm_iris_input_train, sample.id.column = "id", attribute.column = "attribute", value.column = "value1", label.column = "species", max.step = 150, seed = 0 ) # Use the generated model to predict the 'species' on the test data svm_iris_input_test. td_svm_sparse_predict_mle_out <- td_svm_sparse_predict_mle( object = svm_iris_model, newdata = svm_iris_input_test, newdata.partition.column = c("id"), attribute.column = "attribute", sample.id.column = "id", value.column = "value1", accumulate.label = "species" )