Classification edge by resubstitution
Estimate Quality of Classification Tree
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
edge — Classification edge
Classification edge obtained by re-substituting the training data into the calculation of edge, returned as a scalar.
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
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).
Introduced in R2011a