Description
The Decision Tree Predict function applies a tree model to input data, to output predicted labels for each data point.
Usage
td_decision_tree_predict_sqle ( object = NULL, newdata = NULL, attr.table.groupby.columns = NULL, attr.table.pid.columns = NULL, attr.table.val.column = NULL, accumulate = NULL, output.response.probdist = FALSE, output.responses = NULL, newdata.partition.column = NULL, newdata.order.column = NULL ) ## S3 method for class 'td_decision_tree_mle' predict( object = NULL, newdata = NULL, attr.table.groupby.columns = NULL, attr.table.pid.columns = NULL, attr.table.val.column = NULL, accumulate = NULL, output.response.probdist = FALSE, output.responses = NULL, newdata.partition.column = NULL, newdata.order.column = NULL)
Arguments
object |
Required Argument. |
newdata |
Required Argument. |
newdata.partition.column |
Partition By columns for newdata. |
newdata.order.column |
Order By columns for newdata. |
attr.table.groupby.columns |
Required Argument. |
attr.table.pid.columns |
Required Argument. Specifies the names of the columns that define the data point identifiers. |
attr.table.val.column |
Required Argument. |
accumulate |
Optional Argument. |
output.response.probdist |
Optional Argument. |
output.responses |
Required if 'output.response.probdist' is TRUE, otherwise disallowed. Specifies the labels in the input table. |
Value
Function returns an object of class "td_decision_tree_predict_sqle" 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("decisiontreepredict_example", "iris_attribute_test", "iris_attribute_output") loadExampleData("decision_tree_example", "iris_attribute_train", "iris_response_train", "iris_altinput") # Create remote tibble objects. iris_attribute_train <- tbl(con, "iris_attribute_train") iris_response_train <- tbl(con, "iris_response_train") iris_attribute_test <- tbl(con, "iris_attribute_test") # Example - # First train the data, i.e. create a Model decision_tree_out <- td_decision_tree_mle(attribute.name.columns = c("attribute"), attribute.value.column = "attrvalue", id.columns = c("pid"), attribute.table = iris_attribute_train, response.table = iris_response_train, response.column = "response", num.splits = 3, approx.splits = FALSE, nodesize = 10, max.depth = 10, split.measure = "gini" ) #Run predict on the output of td_decision_tree_mle td_decision_tree_predict_out <- td_decision_tree_predict_sqle(object = decision_tree_out, newdata = iris_attribute_test, newdata.partition.column = c("pid"), newdata.order.column = c("attribute"), attr.table.groupby.columns = c("attribute"), attr.table.pid.columns = c("pid"), attr.table.val.column = "attrvalue", accumulate = c("attrvalue") ) # Alternatively use S3 predict function to run predict on the output of td_decision_tree_mle. predict_out <- predict(decision_tree_out, newdata = iris_attribute_test, newdata.partition.column = c("pid"), newdata.order.column = c("attribute"), attr.table.groupby.columns = c("attribute"), attr.table.pid.columns = c("pid"), attr.table.val.column = "attrvalue", accumulate = c("attrvalue") )