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Predictive Modeling Terms. Bagging (Voting, Averaging) The concept of bagging
(voting for classification, averaging for regression ...
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... by defining the problem or question in specific terms that can be ... access, data
exploration and transformation, followed by predictive modeling.” (Lange, 2006 ...
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"Relationship marketing" and the terms "partnership" and "alliance" have ...
... Artificial neural networks: Non-linear predictive models that ... paper provides a glossary
of data mining terms. ... these feats in data mining is called modeling. ...
Submitted by mmeadows on May 22, 2008
Category: Technology
Words: 736 | Pages: 3
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Bagging (Voting, Averaging)
The concept of bagging (voting for classification, averaging for regression-type problems with continuous dependent variables of interest) applies to the area of predictive data mining, to combine the predicted classifications from multiple models, or from the same type of model for different learning data. It is also used to address the inherent instability of results when applying complex models to relatively small data sets. Suppose your data mining task is to build a model for predictive classification, and the dataset from which to train the model (learning data set, which contains observed classifications) is relatively small. You could repeatedly sub-sample (with replacement) from the dataset, and apply, for example, a tree classifier (e.g., C&RT and CHAID) to the successive samples. In practice, very different trees will often be grown for the different samples, illustrating the instability of models often evident with small datasets. One method of deriving a single prediction (for new observations) is to use all trees found in the different samples, and to apply some simple voting: The final classification is the one most often predicted by the different trees. Note that some weighted combination of predictions (weighted vote, weighted average) is also possible, and commonly used. A sophisticated (machine learning) algorithm for generating weights for weighted prediction or voting is the Boosting procedure.
Boosting
Boosting applies to the area of predictive data mining, to generate multiple models or classifiers (for prediction or classification), and to derive weights to combine the predictions from those models into a single prediction or predicted classification.
A simple algorithm for boosting works like this: Start by applying some method (e.g. a tree classifier such as C&RT or CHAID) to the learning data, where each observation is assigned an equal weight. Compute the...
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