Teradata Package for R Function Reference | 17.00 - DecisionTreePredict - Teradata Package for R - Look here for syntax, methods and examples for the functions included in the Teradata Package for R.

DecisionTreePredict

Description

The DecisionTreePredict function applies a tree model to input data, to output predicted labels for each data point.
Note: This function is only available when tdplyr is connected to Vantage 1.1 or later versions.

Usage

  td_decision_tree_predict_mle (
      object = NULL,
      newdata = NULL,
      attr.table.groupby.columns = NULL,
      attr.table.pid.columns = NULL,
      attr.table.val.column = NULL,
      output.response.probdist = FALSE,
      accumulate = NULL,
      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. Specifies the model tbl_teradata generated by DecisionTree (td_decision_tree_mle). This argument can accept either a tbl_teradata or an object of "td_decision_tree_mle" class
object.order.column	Optional Argument. Specifies Order By columns for "object". Values to this argument can be provided as a vector, if multiple columns are used for ordering. Types: character OR vector of Strings (character)
newdata	Required Argument. Specifies the name of the tbl_teradata object containing the attribute names and the values. It contains test data and has same schema as that of input tbl_teradata of td_decision_tree_mle function.
newdata.partition.column	Required Argument. Specifies Partition By columns for "newdata". Values to this argument can be provided as a vector, if multiple columns are used for partition. Types: character OR vector of Strings (character)
newdata.order.column	Optional Argument. Specifies Order By columns for "newdata". Values to this argument can be provided as a vector, if multiple columns are used for ordering. Types: character OR vector of Strings (character)
attr.table.groupby.columns	Required Argument. Specifies the names of the columns on which attribute tbl_teradata used in td_decision_tree_mle function is partitioned. Each partition contains one attribute of the input data. Types: character OR vector of Strings (character)
attr.table.pid.columns	Required Argument. Specifies the names of the columns that define the data point identifiers. Types: character OR vector of Strings (character)
attr.table.val.column	Required Argument. Specifies the name of the column that contains the input values. Types: character
output.response.probdist	Optional Argument. Specifies whether to output probabilities. Note: With Vantage version prior to 1.1.1, when this argument is set to TRUE, the argument "output.responses" must also be specified. It can be set to TRUE only when the td_decision_tree_mle function call used to generate the model had "output.response.probdist" to TRUE. Default Value: FALSE Types: logical
accumulate	Optional Argument. Specifies the names of input tbl_teradata columns to copy to the output tbl_teradata. Types: character OR vector of Strings (character)
output.responses	Optional Argument. Specifies all responses in input tbl_teradata . Note: This argument requires the "output.response.probdist" argument to be set to TRUE. Types: character OR vector of characters
newdata.sequence.column	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "newdata". The argument is used to ensure deterministic results for functions which produce results that vary from run to run. Types: character OR vector of Strings (character)
object.sequence.column	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "object". The argument is used to ensure deterministic results for functions which produce results that vary from run to run. Types: character OR vector of Strings (character)

Value

Function returns an object of class "td_decision_tree_predict_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("decisiontreepredict_example", "iris_attribute_test")
    loadExampleData("decision_tree_example", "iris_attribute_train", "iris_response_train",
                    "iris_altinput")

    # Create object(s) of class "tbl_teradata".
    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 - 1
    # First train the data, i.e. create a Model
    decision_tree_out <- td_decision_tree_mle(attribute.table = iris_attribute_train,
                               response.table = iris_response_train,
                               attribute.name.columns = c("attribute"),
                               attribute.value.column = "attrvalue",
                               id.columns = c("pid"),
                               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() function.
    td_decision_tree_predict_out <- td_decision_tree_predict_mle(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")
                                       )
    # Example - 2
    # set output.response.probdist to TRUE to output the probability distributions.
    decision_tree_out2 <- td_decision_tree_mle(attribute.table=iris_attribute_train,
                                response.table=iris_response_train,
                                attribute.name.columns='attribute',
                                id.columns='pid',
                                attribute.value.column='attrvalue',
                                response.column='response',
                                num.splits=3,
                                nodesize=5,
                                max.depth=10,
                                split.measure='gini',
                                approx.splits=FALSE,
                                output.response.probdist=TRUE,
                                response.probdist.type = "Laplace"
                              )

    # Use "output.response.probdist", "output.responses".
    td_decision_tree_predict_out <- td_decision_tree_predict_mle(newdata=iris_attribute_test,
                                       newdata.partition.column='pid',
                                       newdata.order.column='attribute',
                                       object=decision_tree_out2,
                                       attr.table.groupby.columns='attribute',
                                       attr.table.pid.columns='pid',
                                       attr.table.val.column='attrvalue',
                                       accumulate='attrvalue',
                                       output.response.probdist=TRUE,
                                       output.responses=c('1','2','3')
                                       )