LibSVM (3.7)

A wrapper class for the libsvm tools (the libsvm classes, typically the jar file, need to be in the classpath to use this classifier). LibSVM runs faster than SMO since it uses LibSVM to build the SVM classifier. LibSVM allows users to experiment with One-class SVM, Regressing SVM, and nu-SVM supported by LibSVM tool

LibSVM reports many useful statistics about LibSVM classifier (e.g., confusion matrix,precision, recall, ROC score, etc.).

Yasser EL-Manzalawy (2005). WLSVM. URL

Chih-Chung Chang, Chih-Jen Lin (2001).LIBSVM - A Library for Support Vector Machines.


(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.


LibSVM Options

S: Set type of SVM (default: 0) 0 = C-SVC 1 = nu-SVC 2 = one-class SVM 3 = epsilon-SVR 4 = nu-SVR

K: Set type of kernel function (default: 2) 0 = linear: u'*v 1 = polynomial: (gamma*u'*v + coef0)^degree 2 = radial basis function: exp(-gamma*|u-v|^2) 3 = sigmoid: tanh(gamma*u'*v + coef0)

D: Set degree in kernel function (default: 3)

G: Set gamma in kernel function (default: 1/k)

R: Set coef0 in kernel function (default: 0)

C: Set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default: 1)

N: Set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default: 0.5)

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

J: Turn off nominal to binary conversion. WARNING: use only if your data is all numeric!

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

P: Set the epsilon in loss function of epsilon-SVR (default: 0.1)

M: Set cache memory size in MB (default: 40)

E: Set tolerance of termination criterion (default: 0.001)

H: Turns the shrinking heuristics off (default: on)

W: Set the parameters C of class i to weight[i]*C, for C-SVC. E.g., for a 3-class problem, you could use "1 1 1" for equally weighted classes. (default: 1 for all classes)

B: Generate probability estimates for classification

model: Specifies the filename to save the libsvm-internal model to. Gets ignored if a directory is provided.

seed: Random seed (default = 1)

S: Random number seed. (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: [Nominal class, Binary class, Unary class, Numeric class, Date class] 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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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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