M: Sets the method to use. Valid values are 0 (1-against-all),
1 (random codes), 2 (exhaustive code), and 3 (1-against-1). (default 0)
R: Sets the multiplier when using random codes. (default 2.0)
P: Use pairwise coupling (only has an effect for 1-against1)
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)
F: Set the loss function to minimize.
0 = hinge loss (SVM), 1 = log loss (logistic regression),
2 = squared loss (regression), 3 = epsilon insensitive loss (regression),
4 = Huber loss (regression).
(default = 0)
L: The learning rate. If normalization is
turned off (as it is automatically for streaming data), then the
default learning rate will need to be reduced (try 0.0001).
(default = 0.01).
R: The lambda regularization constant (default = 0.0001)
E: The number of epochs to perform (batch learning only, default = 500)
C: The epsilon threshold (epsilon-insenstive and Huber loss only, default = 1e-3)
N: Don't normalize the data
M: Don't replace missing values
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, Missing class values, Only multi-Instance data]
min # Instance: 0
