Seminar Details
| Date |
4-7-2011 |
| Time |
11:15 |
| Room/Location |
DISI-Sala Conferenze III piano |
| Title |
Random Forests: A Swiss Army Knife Classifier |
| Speaker |
Leif Peterson, Ph.D. |
| Affiliation |
Associate Member The Methodist Hospital Research Institute Director, Center for Biostatistics Associ |
| Link |
http://www.methodisthealth.com/tmhri.cfm?id=37614
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| Abstract |
Random Forest (RF) is a stable classifier which is not biased
toward the amount of training data used, and offers little chance of
overfitting the data.Generalization error is, for the most part, lower
than other classifiers.RF classification accuracy values tend to be
lower than other classifiers because unlike other classifiers RF
randomly samples objects and features used for each decision tree in the
forest.This presentation will introduce class discovery with
unsupervised classification via RF, class prediction with supervised RF
classification, feature importance scores, and object outlier
determination.RF offers many advantages over other approaches including
reduced generalization error, class discovery through unsupervised
cluster prediction, class prediction via supervised learning,
simultaneous interaction effects of multiple features, better handling
of missing data, and rapid learning times. One downturn of RF is that it
is not necessary to perform cross validation, e.g., 10-fold or
leave-one-out, due to the advantage of using bootstrapping.Bootstrapping
has been shown to result in low variance or less variation across data
sets, and potentially greater levels of bias due to its dependence on
the proportion of data used for training
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