Description
The DenseSVMModelPrinter function extracts readable information from the model
produced by the DenseSVMTrainer (td_svm_dense_mle
) function. The function
can display either a summary of the model training results or a tbl_teradata containing
the weights for each attribute.
Usage
td_svm_dense_summary_mle ( object = NULL, data = NULL, attribute.columns = 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.columns |
Required Argument. |
summary |
Optional Argument. |
data.sequence.column |
Optional Argument. |
object.sequence.column |
Optional Argument. |
Value
Function returns an object of class "td_svm_dense_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("svmdense_example", "svm_iris_train") # Create object(s) of class "tbl_teradata". svm_iris_train <- tbl(con, "svm_iris_train") # Generate Radial Basis Model (RBF) Model densesvm_iris_rbf_model <- td_svm_dense_mle(data = svm_iris_train, sample.id.column = "id", attribute.columns = c('sepal_length', 'sepal_width', 'petal_length', 'petal_width'), kernel.function = "rbf", gamma = 0.1, subspace.dimension = 120, hash.bits = 512, label.column = "species", cost = 1, bias = 0, max.step = 100, seed = 1 ) # Example 1 - Display the model parameters (weights, attributes etc). td_svm_dense_summary_out1 <- td_svm_dense_summary_mle(object = densesvm_iris_rbf_model, data = svm_iris_train, attribute.columns=c('sepal_length','sepal_width', 'petal_length','petal_width'), summary = FALSE ) # Example 2 - outputs only summary of the model. td_svm_dense_summary_out2 <- td_svm_dense_summary_mle(object = densesvm_iris_rbf_model, data = svm_iris_train, attribute.columns=c('sepal_length','sepal_width', 'petal_length','petal_width'), summary = TRUE )