LibLINEAR (3.7)

A wrapper class for the liblinear 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 3.7)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.


LibLINEAR Options

S: Set type of solver (default: 1) for multi-class classification 0 -- L2-regularized logistic regression (primal) 1 -- L2-regularized L2-loss support vector classification (dual) 2 -- L2-regularized L2-loss support vector classification (primal) 3 -- L2-regularized L1-loss support vector classification (dual) 4 -- support vector classification by Crammer and Singer 5 -- L1-regularized L2-loss support vector classification 6 -- L1-regularized logistic regression 7 -- L2-regularized logistic regression (dual) for regression 11 -- L2-regularized L2-loss support vector regression (primal) 12 -- L2-regularized L2-loss support vector regression (dual) 13 -- L2-regularized L1-loss support vector regression (dual)

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, L1-regularized logistic regression or L2-regularized logistic regression (dual)!

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

Select target 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

Command line options

It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.

Additional Options

Select optional vector column
If the input table contains vector columns (e.g. double vector), the one to use can be selected here. This vector column will be used as attributes only and all other columns, except the target column, will be ignored.
Keep training instances
If checked, all training instances will be kept and stored with the classifier model. It is useful to calculate additional evaluation measures (see Weka Predictor) that make use of class prior probabilities. If no evaluation is performed or those measures are not required, it is advisable to NOT keep the training instances.

Input Ports

Training data

Output Ports

Trained model

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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