KernelLogisticRegression (3.7)

This classifier generates a two-class kernel logistic regression model

The model is fit by minimizing the negative log-likelihood with a quadratic penalty using BFGS optimization, as implemented in the Optimization class.Alternatively, conjugate gradient optimization can be applied.

The user can specify the kernel function and the value of lambda, the multiplier for the quadractic penalty.Using a linear kernel (the default) this method should give the same result as ridge logistic regression implemented in Logistic, assuming the ridge parameter is set to the same value as lambda, and not too small.

By replacing the kernel function, we can learn non-linear decision boundaries.

Note that the data is filtered using ReplaceMissingValues, RemoveUseless, NominalToBinary, and Standardize (in that order).

If a CachedKernel is used, this class will overwrite the manually specified cache size and use a full cache instead.

To apply this classifier to multi-class problems, use the MultiClassClassifier.

This implementation stores the full kernel matrix at training time for speed reasons.

(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.

Options

KernelLogisticRegression Options

S: Random number seed. (default 1)

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

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

L: The lambda penalty parameter. (default 0.01)

G: Use conjugate gradient descent instead of BFGS.

P: The size of the thread pool, for example, the number of cores in the CPU. (default 1)

E: The number of threads to use, which should be >= size of thread pool. (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, 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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Views

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.

Workflows

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Developers

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Installation

To use this node in KNIME, install the extension KNIME Weka Data Mining Integration (3.7) from the below update site following our NodePit Product and Node Installation Guide:

v5.4

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 5.4.0.v202407231410

On NodePit since: 2024-12-06

Last update: 2024-12-10

KNIME versions: Since v3.6

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