LibLINEAR (3.6)

A wrapper class for the liblinear tools (the liblinear classes, typically the jar file, need to be in the classpath to use this classifier). Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008). LIBLINEAR - A Library for Large Linear Classification. 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: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, Nominal class, Binary class, Missing class values] Dependencies: [] min # Instance: 1

Classifier Options

S: Set type of solver (default: 1) 0 = L2-regularized logistic regression 1 = L2-loss support vector machines (dual) 2 = L2-loss support vector machines (primal) 3 = L1-loss support vector machines (dual) 4 = multi-class support vector machines by Crammer and Singer

C: Set the cost parameter C (default: 1)

Z: Turn on normalization of input data (default: off)

N: Turn on nominal to binary conversion.

M: Turn off missing value replacement. WARNING: use only if your data has no missing values.

P: Use probability estimation (default: off) currently for L2-regularized logistic regression only!

E: Set tolerance of termination criterion (default: 0.01)

W: Set the parameters C of class i to weight[i]*C (default: 1)

B: Add Bias term with the given value if >= 0; if < 0, no bias term added (default: 1)

D: If set, classifier is run in debug mode and may output additional info to the console

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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