Make Predictions from a "LogisticRegression" Object

Similar to other predict methods, this function predicts fitted values from a fitted "LogisticRegression" object.

# S3 method for LogisticRegression
predict(
  model,
  key,
  data,
  features = NULL,
  verbose = FALSE,
  thread.ratio = NULL,
  multi.class = NULL,
  class.map0 = NULL,
  class.map1 = NULL,
  categorical.variable = NULL
)

Arguments

model

R6Class object
A "LogisticRegression" object for prediction.
key

character
Name of the ID column.
data

DataFrame
DataFrame containting the data.
features

character of list of characters, optional
Name of feature columns for prediction.
If not provided, it defaults to all non-key columns of data.
verbose

logical, optional
If TRUE, output all classes and the corresponding confidences for each data point.
Defaults to FALSE.
thread.ratio

double, optional
Controls the proportion of available threads that can be used by this function.
The value range is from 0 to 1, where 0 indicates a single thread, and 1 indicates all available threads.
Values between 0 and 1 will use up to that percentage of available threads.Values outside this range are ignored.
Defaults to 0.
multi.class

logical, optional
If the value is TRUE, prediction for multi-class classification is performed.
Otherwise prediction for binary classification is performed.
Defaults to model$multi.class.
class.map0

character, optional
Categorical label to map to 0.
Only valid when multi.class or model$multi.class is FALSE. class.map0 is mandatory when label column type is VARCHAR or NVARCHAR during binary class fit and score.
Defaults to model$class.map0.
class.map1

character, optional
Categorical label to map to 1.
Only valid when multi.class or model$multi.class is FALSE. class.map1 is mandatory when label column type is VARCHAR or NVARCHAR during binary class fit and score.
Defaults to model$class.map1.
categorical.variable

character or list/vector of characters, optional
Indicates features should be treated as categorical variable.
The default behavior is dependent on what input is given:

  • "VARCHAR" and "NVARCHAR": categorical

  • "INTEGER" and "DOUBLE": continuous.

VALID only for variables of "INTEGER" type, omitted otherwise.
No default value.

Format

S3 methods

Value

Returns a Dataframe
containing predicted values, structured as follows.

  • ID column, with same name and type as `data`'s ID column.

  • CLASS, type NVARCHAR, predicted class name.

  • PROBABILITY, type DOUBLE.

Note

predict() will pass the pmml table to PAL as the model representation if there is a pmml table, or the coefficients table otherwise.

Examples

lr is a "LogisticRegression" object.
Call the function:

> predict(lr, data2, key = "ID", features = list("V1", "V2", "V3"))

See also

hanaml.LogisticRegression