Description
The function creates confusion matrix as XML output string, displaying counts of predicted versus actual values of the dependent variable of the decision tree model. It also contains counts of correct and incorrect predictions. The function also generates two profile DataFrames containing the details about the decisions made during the prediction.
Usage
td_decision_tree_evaluator_valib(model, data, ...)
Arguments
model |
Required Argument. |
data |
Required Argument. |
... |
Specifies other arguments supported by the function as described in the 'Other Arguments' section. |
Value
Function returns an object of class "td_decision_tree_evaluator_valib"
which is a named list containing objects of class "tbl_teradata".
Named list member can be referenced directly with the "$" operator
using names:
result
profile.result.1
profile.result.2
Other Arguments
include.confidence
Optional Argument.
Specifies whether the output tbl_teradata contain a column
indicating how likely it is, for a particular leaf node on
the tree, that the prediction is correct. If not specified
or set to 'FALSE', the confidence column is not created.
Note:
This argument cannot be specified along with "targeted.value"
argument.
Default Value: FALSE
Types: logical
index.columns
Optional Argument.
Specifies one or more different columns for the primary index of
the result output tbl_teradata. By default, the primary index columns
of the result output tbl_teradata are the primary index columns of the
input tbl_teradata "data". In addition, the columns specified in this
argument need to form a unique key for the result output tbl_teradata.
Otherwise, there are more than one score for a given observation.
Types: character OR list of Strings (character)
response.column
Optional Argument.
Specifies the name of the predicted value column. If this argument
is not specified, the name of the dependent column in "data"
tbl_teradata is used.
Types: character
accumulate
Optional Argument.
Specifies one or more columns from the "data" tbl_teradata that can
be passed to the result output tbl_teradata.
Types: character OR list of Strings (character)
Examples
# Notes:
# 1. To execute Vantage Analytic Library functions, set option 'val.install.location' to
# the database name where Vantage analytic library functions are installed.
# 2. Datasets used in these examples can be loaded using Vantage Analytic Library installer.
# Set the option 'val.install.location'.
options(val.install.location = "SYSLIB")
# Get remote data source connection.
con <- td_get_context()$connection
# Create an object of class "tbl_teradata".
df <- tbl(con, "customer_analysis")
print(df)
# Run DecisionTree() on columns "age", "income" and "nbr_children", with dependent
# variable "gender".
dt_obj <- td_decision_tree_valib(data=df,
columns=c("age", "income", "nbr_children"),
response.column="gender",
algorithm="gainratio",
binning=FALSE,
max.depth=5,
num.splits=2,
pruning="gainratio")
# Evaluate the decision tree model generated above.
obj <- td_decision_tree_evaluator_valib(data=df,
model=dt_obj$result,
accumulate=c("city_name", "state_code"))
# Print the results.
print(obj$result)
print(obj$profile.result.1)
print(obj$profile.result.2)