Description
The Decision Forest Predict (td_decision_forest_predict_mle
) function
uses the model generated by the Decision Forest (td_decision_forest_mle
)
function to generate predictions on a response variable for a test set of data.
Note: This function is only available when tdplyr is connected to Vantage 1.1
or later versions.
Usage
td_decision_forest_predict_mle ( object = NULL, newdata = NULL, id.column = NULL, detailed = FALSE, terms = NULL, output.response.probdist = FALSE, output.responses = NULL, newdata.sequence.column = NULL, object.sequence.column = NULL, newdata.order.column = NULL, object.order.column = NULL )
Arguments
object |
Required Argument. |
object.order.column |
Optional Argument. |
newdata |
Required Argument. |
newdata.order.column |
Optional Argument. |
id.column |
Required Argument. |
detailed |
Optional Argument. |
terms |
Optional Argument. |
output.response.probdist |
Optional Argument. |
output.responses |
Optional Argument.
Default Value: NULL |
newdata.sequence.column |
Optional Argument. |
object.sequence.column |
Optional Argument. |
Value
Function returns an object of class "td_decision_forest_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 example data. loadExampleData("decisionforestpredict_example", "housing_test", "housing_train") # Create remote tibble objects. housing_train <- tbl(con, "housing_train") housing_test <- tbl(con, "housing_test") # Example 1 - Using model object generated by td_decision_forest_mle. # Generate DecisionForest model based on train data "housing_train". formula <- (homestyle ~ driveway + recroom + fullbase + gashw + airco + prefarea + price + lotsize + bedrooms + bathrms + stories + garagepl) decision_forest_model <- td_decision_forest_mle(data=housing_train, formula = formula, tree.type="classification", ntree=50, tree.size=100, nodesize=1, variance=0, max.depth=12, maxnum.categorical=20, mtry=3, mtry.seed=100, seed=100 ) # Use the generated model to predict the 'homestyle' on the test data housing_test. df_predict_mle_out1 <- td_decision_forest_predict_mle(object = decision_forest_model, newdata = housing_test, id.column = "sn", detailed = FALSE, terms = c("homestyle") ) # Example 2 - Using tbl_teradata object of the model generated by td_decision_forest_mle. # Use predictive.model tbl_teradata object of the generated model (in Example 1) to predict # the 'homestyle' on the test data housing_test. df_predict_mle_out2 <- td_decision_forest_predict_mle(object = decision_forest_model$predictive.model, newdata = housing_test, id.column = "sn", detailed = FALSE, terms = c("homestyle") ) # Example 3 - Use the generated model to predict the probabilities for 'homestyle' on the test data # 'housing_test' of each sample. df_predict_mle_out1 <- td_decision_forest_predict_mle(object = decision_forest_model, newdata = housing_test, id.column = "sn", detailed = FALSE, output.response.probdist=TRUE, terms = c("homestyle") ) # Example 4 - Use the generated model to predict 'homestyle' in the test data 'housing_test' and # probabilities of each label specified in the output.response argument. When output.responses is # used, output.response.probdist argument must be set to TRUE to get probability values. df_predict_mle_out1 <-td_decision_forest_predict_mle(object = decision_forest_model, newdata = housing_test, id.column = "sn", detailed = FALSE, output.response.probdist = TRUE, output.responses = c('Eclectic','Classic','bungalow'), terms = c("homestyle") )