Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors. For more information, see Alexander Genkin, David D. Lewis, David Madigan (2004). Large-scale bayesian logistic regression for text categorization. URL http://www.stat.rutgers.edu/~madigan/PAPERS/shortFat-v3a.pdf.
(based on WEKA 3.6)
For further options, click the 'More' - button in the dialog.
All weka dialogs have a panel where you can specify classifier-specific parameters.
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: [Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Binary class] Dependencies:  min # Instance: 0
D: Show Debugging Output
P: Distribution of the Prior (1=Gaussian, 2=Laplacian) (default: 1=Gaussian)
H: Hyperparameter Selection Method (1=Norm-based, 2=CV-based, 3=specific value) (default: 1=Norm-based)
V: Specified Hyperparameter Value (use in conjunction with -H 3) (default: 0.27)
R: Hyperparameter Range (use in conjunction with -H 2) (format: R:start-end,multiplier OR L:val(1), val(2), ..., val(n)) (default: R:0.01-316,3.16)
Tl: Tolerance Value (default: 0.0005)
S: Threshold Value (default: 0.5)
F: Number Of Folds (use in conjuction with -H 2) (default: 2)
I: Max Number of Iterations (default: 100)
N: Normalize the data
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