Next: Wrapper Inducers
Some inducers are still being developed or have esoteric uses. We
briefly mention them.
- Accuracy estimator
- is a wrapper inducer that runs a given
inducer in ACC_INDUCER, estimates its accuracy using an accuracy
estimation method, and returns that as the resulting accuracy.
AccEst utility provides a friendlier interface, but there are
occasions where one wants to do two levels of accuracy estimation
, cross-validation accuracy on holdout sets), where this
inducer is very useful .
- always predicts unknown and thus gets 0% accuracy. It
is mostly used internally, but can be used with the Inducer utility
and DISP_CONFUSION_MAT set to true in order to view the
distribution of the labels. The ``info'' utility is probably a better
way of getting basic statistics about a data file.
- is an inducer for building oblivious decision graphs top-down
. Cannot handle unknown values.
- is a lazy decision tree algorithm, described in
- searches for an attribute ordering. Very researchy.
- is a wrapper discretizer that searches for the best
number of intervals for each attribute. Very slow.
- is a wrapper discretizer that searches for the
best weight for each attribute (from a uniform set of weights).
Slow. Researchy. Not much improvement over feature subset
- Builds decision trees with categorizers you choose at
the leaves. Researchy. Requires that inducers support copies,
which very few do ( e.g.
Sun Oct 6 23:17:50 PDT 1996