score.LogisticRegression.Rd
This function predicts class labels of labeled dataset from a fitted "LogisticRegression" object, and return the corresponding accuracy score.
# S3 method for LogisticRegression
score(
model,
data,
key,
features = NULL,
label = NULL,
thread.ratio = NULL,
multi.class = NULL,
class.map0 = NULL,
class.map1 = NULL,
categorical.variable = NULL,
...
)
S3
methods
R6Class object
A "LogisticRegression" object for scoring.
character
Specifies the ID column in data
.
character or a list of characters, optional
Specifies the feature columns in data
.
The specified features must be the same as those in the model training phase.
Defaults to all non-key, non-label columns in data
if not provided.
character, optional
Specifies the label column in data
.
If not provided, defaults to the last non-key column in data
.
numeric, optional
Specified the number of threads used in model prediction.
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 the range from 0 to 1 are ignored, and the actual number of threads
used is then be heuristically determined.
Defaults to -1.
#@param 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$multi.class
.
character, optional
Categorical label to map to 0.
Only valid when multi.class
or model$multi.class
is FALSE.
class.map0
is mandatory when label column type is VARCHAR or
NVARCHAR for binary classification.
Defaults to model$class.map0
.
character, optional
Categorical label to map to 1.
Only valid when multi.class
or model$multi.class
is FALSE.
class.map1 is mandatory when label column type is VARCHAR or
NVARCHAR for binary classification.
Defaults to model$class.map1
.
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.
numeric
The accuracy score of model
applied to data
.