DecisionForestPredict
Description
The DecisionForestPredict function uses the model generated by the DecisionForest
(td_decision_forest_mle
) function to generate predictions
on a response variable for a test set of data. The model can be stored in either
a tbl_teradata or a DecisionForest object.
Usage
td_decision_forest_predict_mle_sqle (
object = NULL,
newdata = NULL,
id.column = NULL,
detailed = FALSE,
terms = NULL,
newdata.order.column = NULL,
object.order.column = NULL
)
## S3 method for class 'td_decision_forest_mle'
predict(
object = NULL,
newdata = NULL,
id.column = NULL,
detailed = FALSE,
terms = 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. |
Value
Function returns an object of class "td_decision_forest_predict_mle_sqle"
which is a named list containing object of class "tbl_teradata".
Named list member can be referenced directly with the "$" operator
using the name: result.
Examples
# Get the current context/connection.
con <- td_get_context()$connection
# Load example data.
loadExampleData("decisionforestpredict_example", "housing_test", "housing_train")
# Create object(s) of class "tbl_teradata".
housing_test <- tbl(con, "housing_test")
housing_train <- tbl(con, "housing_train")
# Example 1 -
# First train the data, i.e., create a decision forest Model.
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
)
# Run predict on the output of td_decision_forest_mle() function.
td_decision_forest_predict_out <- td_decision_forest_predict_mle_sqle(
object = decision_forest_model,
newdata = housing_test,
id.column = "sn",
detailed = FALSE,
terms = c("homestyle")
)
# Alternatively use the predict S3 method to find predictions.
predict_out <- predict(decision_forest_model,
newdata = housing_test,
id.column = "sn",
detailed = FALSE,
terms = c("homestyle")
)