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, Missing values, Nominal class, 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, Nominal class, Binary class, Unary class, Empty nominal class, Numeric class, Date class, String class, Relational class, Missing class values, Only multi-Instance data]
min # Instance: 1
F: The number of folds for splitting the training set into
smaller chunks for the base classifier.
(default 10)
verbose: Whether to print some more information during building the
classifier.
(default is off)
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.SMO)
:
D: If set, classifier is run in debug mode and
may output additional info to the console
no-checks: Turns off all checks - use with caution!
Turning them off assumes that data is purely numeric, doesn't
contain any missing values, and has a nominal class. Turning them
off also means that no header information will be stored if the
machine is linear. Finally, it also assumes that no instance has
a weight equal to 0.
(default: checks on)
C: The complexity constant C. (default 1)
N: Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)
L: The tolerance parameter. (default 1.0e-3)
P: The epsilon for round-off error. (default 1.0e-12)
M: Fit logistic models to SVM outputs.
V: The number of folds for the internal
cross-validation. (default -1, use training data)
W: The random number seed. (default 1)
K: The Kernel to use.
(default: weka.classifiers.functions.supportVector.PolyKernel)
:
D: Enables debugging output (if available) to be printed.
(default: off)
no-checks: Turns off all checks - use with caution!
(default: checks on)
C: The size of the cache (a prime number), 0 for full cache and
-1 to turn it off.
(default: 250007)
E: The Exponent to use.
(default: 1.0)
L: Use lower-order terms.
(default: no)
