Synopsis#

Incremental Bagging

BibTeX#

@article{N.Oza2001,
   author = {N.Oza, S. Russell},
   journal = {Artificial Intelligence and Statistics},
   pages = {105-112},
   publisher = {Morgan Kaufmann},
   title = {Online bagging and boosting},
   year = {2001}
}

Options#

• -I <num>

  Sets the number of models (default 10)

• -P <size percentage>

  Size of each bag, as a percentage of total training size (default 67)

• -S <seed>

  Random number seed for sampling (default 1)

• -W <classifier name>

  Full name of base classifier. (default: meka.classifiers.multilabel.incremental.BRUpdateable)

• -output-debug-info

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

• -do-not-check-capabilities

  If set, classifier capabilities are not checked before classifier is built (use with caution).

• -num-decimal-places

  The number of decimal places for the output of numbers in the model (default 2).

• -batch-size

  The desired batch size for batch prediction (default 100).

Options specific to classifier meka.classifiers.multilabel.incremental.BRUpdateable:

• -W <classifier name>

  Full name of base classifier. (default: weka.classifiers.trees.HoeffdingTree)

• -output-debug-info

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

• -do-not-check-capabilities

  If set, classifier capabilities are not checked before classifier is built (use with caution).

• -num-decimal-places

  The number of decimal places for the output of numbers in the model (default 2).

• -batch-size

  The desired batch size for batch prediction (default 100).

Options specific to classifier weka.classifiers.trees.HoeffdingTree:

• -L

  The leaf prediction strategy to use. 0 = majority class, 1 = naive Bayes, 2 = naive Bayes adaptive. (default = 2)

• -S

  The splitting criterion to use. 0 = Gini, 1 = Info gain (default = 1)

• -E

  The allowable error in a split decision - values closer to zero will take longer to decide (default = 1e-7)

• -H

  Threshold below which a split will be forced to break ties (default = 0.05)

• -M

  Minimum fraction of weight required down at least two branches for info gain splitting (default = 0.01)

• -G

  Grace period - the number of instances a leaf should observe between split attempts (default = 200)

• -N

  The number of instances (weight) a leaf should observe before allowing naive Bayes to make predictions (NB or NB adaptive only) (default = 0)

• -P

  Print leaf models when using naive Bayes at the leaves.