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.


classifly 0.4

  • Simplified dependencies and structured better with imports instead of depends.

Reference manual

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0.4 by Hadley Wickham, 8 years ago


Browse source code at https://github.com/cran/classifly

Authors: Hadley Wickham <[email protected]>

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports class, stats, plyr

Suggests e1071, rggobi, rpart, MASS

See at CRAN