Description
The SVMSparseSummary 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,
data.sequence.column = NULL,
object.sequence.column = NULL,
data.order.column = NULL,
object.order.column = NULL
)
Arguments
object |
Required Argument. |
object.order.column |
Optional Argument. |
data |
Required Argument. |
data.order.column |
Optional Argument. |
attribute.column |
Required Argument. |
summary |
Optional Argument. |
data.sequence.column |
Optional Argument. |
object.sequence.column |
Optional Argument. |
Value
Function returns an object of class "td_svm_sparse_summary_mle" which
is a named list containing object of class "tbl_teradata".
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 object(s) of class "tbl_teradata".
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
)