BayesianLogisticRegression (3.6)

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

(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.


Class column
Choose the column that contains the target variable.
Preliminary Attribute Check

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

Classifier Options

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

Input Ports

Training data

Output Ports

Trained classifier

Popular Predecessors

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Popular Successors

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Weka Node View
Each Weka node provides a summary view that provides information about the classification. If the test data contains a class column, an evaluation is generated.


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