| Library: | xclust |
| See also: | cartsplitclass cartsplitregr |
| Quantlet: | cartpredict | |
| Description: | classifies the data in accordance with constructed Tree. |
| Usage: | ClassVector = cartpredict(Tree,Data) | |
| Input: | ||
| Tree | Complex tree structure, list of different parameters Tree can be generated using CartClassificationSplit or CartRegressionSplit commands | |
| Data | new observations to classify [f x k], should meet dimensions of Learning Sample | |
| Output: | ||
| ClassVector | vector of classes / response value with dimensions [f x 1]. | |
library("xclust");
x1 = #(0.898473738, 0.852630806, 0.539114276, 0.241251175, 0.476125641, 0.284315731, 0.0181318590)
x2 = #(0.255756912, 0.303028656, 0.146633917, 0.121365982, 0.4111506, 0.97609203, 0.514180304)
x = x1~x2;
y = #(1, 1, 1, 0, 1, 2, 1)
tr = cartsplitclass(x,y,0,1);
newdata = #(1, -2)~#(2, -3)
classvector = cartpredict(tr, newdata)
classvector
Contents of classvector [1,] 2 [2,] 0