Description
Linear Regression Scoring is the application of a Linear Regression model to
an input data that contains the same independent variable columns contained
in the model. The result is an output score data that minimally contains one
or more key columns and an estimate of the dependent variable in the
model.
Some of the key features of linear scoring are outlined below:
If one or more group by columns are present in the input data to be scored and the model input data, each row in the input data to be scored is scored using the appropriate model in the model input data.
If an error such as "Constant columns detected" occurs for a particular combination of group by column values, the predicted value of the dependent column is null for any row containing that combination of group by column values. The error message is also placed in the column name in the model report.
Usage
td_lin_reg_predict_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_lin_reg_predict_valib"
which is a named list containing object of class "tbl_teradata".
Named list member can be referenced directly with the "$" operator
using name: result.
Other Arguments
index.columns
Optional Argument.
Specifies the name(s) of the column(s) representing
the primary index of the score output. By default,
the primary index columns of the score output are
the primary index columns of the input. In
addition, the index columns need to form a unique
key for the score output. Otherwise, there are
more than one score for a given observation.
Types: character OR vector of Strings (character)
response.column
Optional Argument.
Specifies the name of the predicted value column.
If not used, the name of the dependent column in
the input is used.
Note:
If the response column is not unique in the score output, '_tm_' is automatically placed in front of the name.
Types: character
accumulate
Optional Argument.
Specifies the name(s) of the column(s) from the input
to retain in the output.
Types: character OR vector of Strings (character)
residual.column
Optional Argument.
Specifies the name of a column that contains the
residual value (the difference between the
predicted and actual value of the dependent
variable).
Default Value: 'Residual'
Types: character
Examples
# Notes:
# 1. To execute Vantage Analytic Library functions, set option
# 'val.install.location' to the database name where Vantage analytic
# library functions are installed.
# 2. Datasets used in these examples can be loaded using Vantage Analytic
# Library installer.
# Set the option 'val.install.location'.
options(val.install.location = "SYSLIB")
# Get remote data source connection.
con <- td_get_context()$connection
# Create an object of class "tbl_teradata".
df <- tbl(con, "customer")
print(df)
# Example 1: Shows how linear regression model scoring is performed.
# First generate the model using td_lin_reg_valib() function.
lin_reg_obj <- td_lin_reg_valib(data=df,
columns=c("age", "years_with_bank",
"nbr_children"),
response.column="income")
# Print the model.
print(lin_reg_obj$model)
# Score the data using the linear regression model generated above.
obj <- td_lin_reg_predict_valib(data=df,
model=lin_reg_obj$model,
response.column="inc")
# Print the results.
print(obj$result)
# Score using S3 predict function and the model generated above.
obj <- predict(object=lin_reg_obj,
data=df,
residual.column="residual_col")
# Print the results.
print(obj$result)