Class for performing additive logistic regression. This class performs classification using a regression scheme as the base learner, and can handle multi-class problems. For more information, see J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University. Can do efficient internal cross-validation to determine appropriate number of iterations.
(based on WEKA 3.6)
For further options, click the 'More' - button in the dialog.
All weka dialogs have a panel where you can specify classifier-specific parameters.
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, Missing values, Nominal class, Binary class, Missing class values] Dependencies: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, String attributes, Relational attributes, Missing values, No class, Missing class values, Only multi-Instance data] min # Instance: 1
Q: Use resampling instead of reweighting for boosting.
P: Percentage of weight mass to base training on. (default 100, reduce to around 90 speed up)
F: Number of folds for internal cross-validation. (default 0 -- no cross-validation)
R: Number of runs for internal cross-validation. (default 1)
L: Threshold on the improvement of the likelihood. (default -Double.MAX_VALUE)
H: Shrinkage parameter. (default 1)
S: Random number seed. (default 1)
I: Number of iterations. (default 10)
D: If set, classifier is run in debug mode and may output additional info to the console
W: Full name of base classifier. (default: weka.classifiers.trees.DecisionStump)
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D: If set, classifier is run in debug mode and may output additional info to the console
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