Classification edge by resubstitution
edge = resubEdge(tree)
returns the classification edge obtained by
edge = resubEdge(
tree on its training
A classification tree created using
Classification edge obtained by resubstituting the training data into the calculation of edge.
Estimate the quality of a classification tree for the Fisher iris data by resubstitution.
load fisheriris tree = fitctree(meas,species); redge = resubEdge(tree)
redge = 0.9384
The edge is the weighted mean value of the classification
margin. The weights are the class probabilities in
The classification margin is the difference between the
classification score for the true class and maximal
classification score for the false classes. Margin is a column vector with the same
number of rows as in the matrix
For trees, the score of a classification of a leaf node is the posterior probability of the classification at that node. The posterior probability of the classification at a node is the number of training sequences that lead to that node with the classification, divided by the number of training sequences that lead to that node.
For an example, see Posterior Probability Definition for Classification Tree.
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).