classifly: Explore classification models in high dimensions

Given $p$-dimensional training data containing $d$ groups (the design space), a classification algorithm (classifier) predicts which group new data belongs to. Generally the input to these algorithms is high dimensional, and the boundaries between groups will be high dimensional and perhaps curvilinear or multi-faceted. This package implements methods for understanding the division of space between the groups.

AuthorHadley Wickham <h.wickham@gmail.com>
Date of publication2014-04-23 19:51:22
MaintainerHadley Wickham <h.wickham@gmail.com>
LicenseMIT + file LICENSE
Version0.4
http://had.co.nz/classifly

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Files in this package

classifly
classifly/NAMESPACE
classifly/CHANGELOG
classifly/NEWS
classifly/data
classifly/data/olives.tab
classifly/R
classifly/R/rescaler.R
classifly/R/classification.r
classifly/R/olives.R
classifly/R/explore.r
classifly/R/data.r
classifly/R/knn.r
classifly/MD5
classifly/DESCRIPTION
classifly/man
classifly/man/explore.Rd classifly/man/olives.Rd classifly/man/classify.Rd classifly/man/classifly.Rd classifly/man/simvar.Rd classifly/man/variables.Rd classifly/man/advantage.Rd classifly/man/generate_data.Rd classifly/man/posterior.Rd classifly/man/generate_classification_data.Rd classifly/man/knnf.Rd
classifly/LICENSE

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