Explore Classification Models in High Dimensions
Calculate the advantage the most likely class has over the next most l...
Classifly provides a convenient method to fit a classification functio...
Extract classifications from a variety of methods.
Default method for exploring objects
Generate classification data.
Generate new data from a data frame.
A wrapper function for knn to allow use with classifly.
Extract posterior group probabilities
Simulate observations from a vector
Extract predictor and response variables for a model object.
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.