Predicts class labels across a 2-D grid using the complete tree (not
individual node SVMs), then overlays the original data points. Because
predictions come from svm_predict_tree, multiclass trees are
handled correctly - each grid cell receives the final leaf prediction which
respects all OVR splits along the path.
plot_surface(tree, data, response, features = NULL, resolution = 200)An svmodt_node object returned by svm_split.
The original training data frame.
Character string naming the response column in data.
Character vector of length 2 giving the two features to plot on the x and y axes. Defaults to the first two features used at the root.
Integer; grid resolution per axis (default 200).
Higher values give smoother region boundaries.
A ggplot2 object. The background tiles show the predicted class for each grid cell; points show true class labels.
All features not used as plot axes are held fixed at their in-sample median
(numeric) or mode (categorical). You choose which two features to plot via
features; if omitted the first two features used at the root node are
used.