Description
The AdaBoostPredict (td_adaboost_predict_mle
) function applies the model
output by the AdaBoost (td_adaboost_mle
) function to a new data set,
outputting predicted labels for each data point.
Usage
td_adaboost_predict_mle ( object = NULL, newdata = NULL, attr.groupby.columns = NULL, attr.pid.columns = NULL, attr.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) ## S3 method for class 'td_adaboost_mle' predict( object = NULL, newdata = NULL, attr.groupby.columns = NULL, attr.pid.columns = NULL, attr.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. |
object.order.column |
Required Argument. |
newdata |
Required Argument. |
newdata.partition.column |
Required Argument. |
newdata.order.column |
Optional Argument. |
attr.groupby.columns |
Required Argument. |
attr.pid.columns |
Required Argument. |
attr.val.column |
Required Argument. |
output.response.probdist |
Optional Argument. |
accumulate |
Optional Argument. |
output.responses |
Optional Argument. |
newdata.sequence.column |
Optional Argument. |
object.sequence.column |
Optional Argument. |
Value
Function returns an object of class "td_adaboost_predict_mle" 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 the data to run the example loadExampleData("adaboost_example", "housing_train", "housing_cat", "housing_train_response", "iris_attribute_train", "iris_response_train") loadExampleData("adaboost_predict_example", "housing_test", "iris_attribute_test") # 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") housing_test <- tbl(con, "housing_test") iris_attribute_test <- tbl(con, "iris_attribute_test") # Example 1 - This example uses test data and the model output by the td_adaboost_mle # to use real estate sales data to predict home style. # # First, we will have to run td_adaboost_mle on the input in sparse format. # We run td_unpivot_mle to create the input in sparse format . td_unpivot_out1 <- 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_out1$result, attribute.name.columns = "attribute", attribute.value.column = "value_col", categorical.attribute.data = housing_cat, response.data = housing_train_response, id.columns = "sn", response.column = "response", iter.num = 20, num.splits = 10, max.depth = 3, min.node.size = 100) # Use the generated model to predict the house style, on the test data. # Transform the test data into sparse format. td_unpivot_out2 <- td_unpivot_mle(data = housing_test, unpivot = c("price", "lotsize", "bedrooms", "bathrms", "stories","driveway", "recroom", "fullbase", "gashw", "airco", "garagepl", "prefarea"), accumulate = "sn") td_adaboost_predict_out1 <- td_adaboost_predict_mle (object = td_adaboost_out1, newdata = td_unpivot_out2$result, attr.groupby.columns = 'attribute', attr.pid.columns = 'sn', attr.val.column = 'value_col', newdata.partition.column = "sn", object.order.column="classifier_id") # Example 2 - In this example, we predict the 'species' for the flowers # represented by the data points in the test data (iris_attribute_test) based on the # model generated using td_adaboost_mle function. # # Train an AdaBoost model on the input data which is in sparse format. 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) # Use the generated model to predict the species on the test data which is # sparse format. td_adaboost_predict_out2 <- td_adaboost_predict_mle (object = td_adaboost_out2, newdata = iris_attribute_test, attr.groupby.columns = 'attribute', attr.pid.columns = 'pid', attr.val.column = 'attrvalue', newdata.partition.column = "pid", output.response.probdist = TRUE, output.responses = c('1','2','3'), object.order.column="classifier_id") # Using S3 predict function to predict the species on the test data based # on the generated model 'td_adaboost_out2'. td_adaboost_predict_out3 <- predict(object = td_adaboost_out2, newdata = iris_attribute_test, attr.groupby.columns = 'attribute', attr.pid.columns = 'pid', attr.val.column = 'attrvalue', newdata.partition.column = "pid", output.response.probdist = TRUE, output.responses = c('1','2','3'), object.order.column="classifier_id")