SMOreg (3.7)

SMOreg implements the support vector machine for regression

The parameters can be learned using various algorithms.The algorithm is selected by setting the RegOptimizer.

The most popular algorithm (RegSMOImproved) is due to Shevade, Keerthi et al and this is the default RegOptimizer.

For more information see:

S.K.Shevade, S.S.

Keerthi, C.Bhattacharyya, K.R.K.

Murthy: Improvements to the SMO Algorithm for SVM Regression.In: IEEE Transactions on Neural Networks, 1999.

A.J. Smola, B. Schoelkopf (1998). A tutorial on support vector regression.

(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.


SMOreg Options

C: The complexity constant C. (default 1)

N: Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)

I: Optimizer class used for solving quadratic optimization problem (default weka.classifiers.functions.supportVector.RegSMOImproved)

K: The Kernel to use. (default: weka.classifiers.functions.supportVector.PolyKernel)

T: The tolerance parameter for checking the stopping criterion. (default 0.001)

V: Use variant 1 of the algorithm when true, otherwise use variant 2. (default true)

P: The epsilon for round-off error. (default 1.0e-12)

L: The epsilon parameter in epsilon-insensitive loss function. (default 1.0e-3)

W: The random number seed. (default 1)

D: Enables debugging output (if available) to be printed. (default: off)

no-checks: Turns off all checks - use with caution! (default: checks on)

C: The size of the cache (a prime number), 0 for full cache and -1 to turn it off. (default: 250007)

E: The Exponent to use. (default: 1.0)

L: Use lower-order terms. (default: no)

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, Missing values, Numeric class, Date class, Missing class values] Dependencies: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, String attributes, Relational attributes] 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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