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

Description

The AdaBoost function takes a training data set and a single decision tree and uses adaptive boosting to produce a strong classifying model that can be input to the function AdaBoostPredict (td_adaboost_predict_mle).

Usage

  td_adaboost_mle (
      attribute.data = NULL,
      attribute.name.columns = NULL,
      attribute.value.column = NULL,
      categorical.attribute.data = NULL,
      response.data = NULL,
      id.columns = NULL,
      response.column = NULL,
      iter.num = 20,
      num.splits = 10,
      approx.splits = TRUE,
      split.measure = "gini",
      max.depth = 3,
      min.node.size = 100,
      output.response.probdist = FALSE,
      categorical.encoding = "graycode",
      attribute.data.sequence.column = NULL,
      response.data.sequence.column = NULL,
      categorical.attribute.data.sequence.column = NULL
  )

Arguments

Value

Function returns an object of class "td_adaboost_mle" which is a named list containing Teradata tbl objects.
Named list members can be referenced directly with the "$" operator using following names:

1. model.table

2. output

Examples

    # Get the current context/connection
    con <- td_get_context()$connection
    # Load the data to run the example
    loadExampleData("adaboost_example", "housing_train", "housing_cat", "housing_train_response", "iris_attribute_train", "iris_response_train")
    
    # Create remote tibble objects.
    housing_train <- tbl(con, "housing_train")
    housing_cat <- tbl(con, "housing_cat")
    housing_train_response <- tbl(con, "housing_train_response")
    iris_attribute_train <- tbl(con, "iris_attribute_train")
    iris_response_train <- tbl(con, "iris_response_train")
    
    # Example 1 - This example uses home sales data to create a model that predicts home 
    # style when input to td_adaboost_predict_mle.
    #
    # Input description:
    # housing_train               (attribute.data) : tbl_teradata containing real estate
    #                                                sales data. There are six numerical
    #                                                predictors and six categorical
    #                                                predictors. The response variable
    #                                                is 'homestyle'.
    # housing_cat     (categorical.attribute.data) : tbl_teradata that lists all the
    #                                                categorical predictors.
    # housing_response             (response.data) : tbl_teradata that lists the responses
    #                                                for each instance in 'attribute.data' as
    #                                                specified by 'id.columms'.
    td_unpivot_out <- td_unpivot_mle(data = housing_train,
                                     unpivot = c("price", "lotsize", "bedrooms", "bathrms",
                                                 "stories","driveway", "recroom", "fullbase",
                                                 "gashw", "airco", "garagepl", "prefarea"),
                                     accumulate = "sn")
                                      
    td_adaboost_out1 <- td_adaboost_mle(attribute.data = td_unpivot_out$result,
                                        attribute.name.columns = "attribute",
                                        attribute.value.column = "value_col",
                                        categorical.attribute.data = housing_cat,
                                        response.data = housing_train_response,
                                        id.columns = "sn",
                                        response.column = "response",
                                        iter.num = 20,
                                        num.splits = 10,
                                        max.depth = 3,
                                        min.node.size = 100)

    
    # Example 2 - This example uses the iris flower dataset to create a model that predicts
    # the species when input to td_adaboost_predict_mle.
    #
    # Input description:
    # iris_attribute_train  (attribute.data) : tbl_teradata containing the iris flower
    #                                          dataset in the sparse format.
    # iris_response_train    (response.data) : tbl_teradata specifying the response variable
    #                                          for each instance.
    td_adaboost_out2 <- td_adaboost_mle(attribute.data = iris_attribute_train,
                                        attribute.name.columns = "attribute",
                                        attribute.value.column = "attrvalue",
                                        response.data = iris_response_train,
                                        id.columns = "pid",
                                        response.column = "response",
                                        iter.num = 5,
                                        num.splits = 10,
                                        max.depth = 3,
                                        min.node.size = 5,
                                        output.response.probdist = TRUE,
                                        approx.splits = FALSE)