Description
The SVMSparseSummary (td_svm_sparse_summary_mle
) function takes the
training data and the model generated by the function SparseSVMTrainer
(td_svm_sparse_mle
) and displays specified information.
Usage
td_svm_sparse_summary_mle ( object = NULL, data = NULL, attribute.column = NULL, summary = FALSE )
Arguments
object |
Required Argument. |
data |
Required Argument. |
attribute.column |
Required Argument. |
summary |
Optional Argument. |
Value
Function returns an object of class "td_svm_sparse_summary_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("svm_sparse_summary_example", "svm_iris_input_train") # Create remote tibble objects. svm_iris_input_train <- tbl(con, "svm_iris_input_train") # Example 1 - Get the summary of the SVM Sparse model. # Create the model svm_train <- 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 ) # Get the summary of the model. sparse_summary_out <- td_svm_sparse_summary_mle(data = svm_iris_input_train, object = svm_train, attribute.column = "attribute", summary = FALSE )