Package
weka.classifiers.bayes
Synopsis
Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors.
For more information, see
Alexander Genkin, David D. Lewis, David Madigan (2004). Large-scale bayesian logistic regression for text categorization. URL http://www.stat.rutgers.edu/~madigan/PAPERS/shortFat-v3a.pdf.
Available in Weka 3.6.x - 3.7.1. Available via the package management system for Weka >= 3.7.2 (bayesianLogisticRegression).
Options
The table below describes the options available for BayesianLogisticRegression.
Option | Description |
---|---|
debug | Turns on debugging mode. |
hyperparameterRange | Hyperparameter value range. In case of CV-based Hyperparameters, you can specify the range in two ways: |
hyperparameterSelection | Select the type of Hyperparameter to be used. |
hyperparameterValue | Specific hyperparameter value. Used when the hyperparameter selection method is set to specific value |
maxIterations | The maximum number of iterations to perform. |
normalizeData | Normalize the data. |
numFolds | The number of folds to use for CV-based hyperparameter selection. |
priorClass | The type of prior to be used. |
threshold | Set the threshold for classifiction. The logistic function doesn't return a class label but an estimate of p(y=+1|B, x(i)). These estimates need to be converted to binary class label predictions. values above the threshold are assigned class +1. |
tolerance | This value decides the stopping criterion. |
Capabilities
The table below describes the capabilites of BayesianLogisticRegression.
Capability | Supported |
---|---|
Class | Binary class |
Attributes | Empty nominal attributes, Unary attributes, Numeric attributes, Binary attributes |
Min # of instances | 0 |