The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.
Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.
Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, Missing values, Binary class, Missing class values]
Dependencies: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, String attributes, Relational attributes, Missing values, No class, Missing class values, Only multi-Instance data]
min # Instance: 1
C: The class for which threshold is determined. Valid values are:
1, 2 (for first and second classes, respectively), 3 (for whichever
class is least frequent), and 4 (for whichever class value is most
frequent), and 5 (for the first class named any of "yes","pos(itive)"
"1", or method 3 if no matches). (default 5).
X: Number of folds used for cross validation. If just a
hold-out set is used, this determines the size of the hold-out set
(default 3).
R: Sets whether confidence range correction is applied. This
can be used to ensure the confidences range from 0 to 1.
Use 0 for no range correction, 1 for correction based on
the min/max values seen during threshold selection
(default 0).
E: Sets the evaluation mode. Use 0 for
evaluation using cross-validation,
1 for evaluation using hold-out set,
and 2 for evaluation on the
training data (default 1).
M: Measure used for evaluation (default is FMEASURE).
manual: Set a manual threshold to use. This option overrides
automatic selection and options pertaining to
automatic selection will be ignored.
(default -1, i.e. do not use a manual threshold).
S: Random number seed.
(default 1)
D: If set, classifier is run in debug mode and
may output additional info to the console
W: Full name of base classifier.
(default: weka.classifiers.functions.Logistic)
:
D: Turn on debugging output.
R: Set the ridge in the log-likelihood.
M: Set the maximum number of iterations (default -1, until convergence).
