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.
|Author||Hadley Wickham <firstname.lastname@example.org>|
|Date of publication||2014-04-23 19:51:22|
|Maintainer||Hadley Wickham <email@example.com>|
|License||MIT + file LICENSE|
advantage: Calculate the advantage the most likely class has over the...
classifly: Classifly provides a convenient method to fit a...
classify: Extract classifications from a variety of methods.
explore: Default method for exploring objects
generate_classification_data: Generate classification data.
generate_data: Generate new data from a data frame.
knnf: A wrapper function for 'knn' to allow use with classifly.
posterior: Extract posterior group probabilities
simvar: Simulate observations from a vector
variables: Extract predictor and response variables for a model object.