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.