Description
The DecisionForestEvaluator function analyzes the model generated by the
DecisionForest (td_decision_forest_mle
) function and assigns weights
to the model variables. The variable/attribute counts in each tree level help you
understand the importance of different variables in the
decision-making process.
Usage
td_decision_forest_evaluator_mle (
object = NULL,
num.levels = 5,
object.sequence.column = NULL,
object.order.column = NULL
)
Arguments
object |
Required Argument. |
object.order.column |
Optional Argument. |
num.levels |
Optional Argument. |
object.sequence.column |
Optional Argument. |
Value
Function returns an object of class "td_decision_forest_evaluator_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("decisionforest_example","housing_train")
# Create object(s) of class "tbl_teradata".
housing_train <- tbl(con, "housing_train")
# First train the data, i.e., create a decision forest model
td_decision_forest_out <- td_decision_forest_mle(data = housing_train,
formula = (homestyle ~ bedrooms + lotsize + gashw + driveway +
stories + recroom + price + garagepl +
bathrms + fullbase + airco + prefarea),
tree.type = "classification",
nodesize = 1,
variance = 0,
max.depth = 12,
maxnum.categorical = 20,
mtry = 100,
seed = 100,
ntree = 50
)
# Analyze generated output.
decision_forest_evaluator_out <- td_decision_forest_evaluator_mle(
object = td_decision_forest_out,
num.levels = 5
)