Compute the classification margin for the Fisher iris data, trained on its first two columns of data, and view the last 10 entries.
load fisheriris X = meas(:,1:2); tree = fitctree(X,species); M = margin(tree,X,species); M(end-10:end)
ans = 0.1111 0.1111 0.1111 -0.2857 0.6364 0.6364 0.1111 0.7500 1.0000 0.6364 0.2000
The classification tree trained on all the data is better.
tree = fitctree(meas,species); M = margin(tree,meas,species); M(end-10:end)
ans = 0.9565 0.9565 0.9565 0.9565 0.9565 0.9565 0.9565 0.9565 0.9565 0.9565 0.9565
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.
Calculate with arrays that have more rows than fit in memory.
This function fully supports tall arrays. For more information, see Tall Arrays.
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
Usage notes and limitations:
marginfunction does not support decision tree models trained with surrogate splits.
For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).