label = resubPredict(tree)
[label,posterior] = resubPredict(tree)
[label,posterior,node] = resubPredict(tree)
[label,posterior,node,cnum] = resubPredict(tree)
[label,...] = resubPredict(tree,Name,Value)
A classification tree constructed by
Specify optional comma-separated pairs of
Name is the argument
Value is the corresponding
Name must appear
inside single quotes (
You can specify several name and value pair
arguments in any order as
'Subtrees'— Pruning level0 (default) | vector of nonnegative integers |
Pruning level, specified as the comma-separated pair consisting
'Subtrees' and a vector of nonnegative integers
in ascending order or
If you specify a vector, then all elements must be at least
the full, unpruned tree and
the completely pruned tree (i.e., just the root node).
If you specify
on all subtrees (i.e., the entire pruning sequence). This specification
is equivalent to using
each level indicated in
Subtrees, and then estimates
the corresponding output arguments. The size of
the size of some output arguments.
Subtrees, the properties
be nonempty. In other words, grow
tree by setting
or by pruning
Matrix or array of posterior probabilities for classes
The node numbers of
The class numbers that
The posterior probability of the classification at a node is the number of training sequences that lead to that node with this classification, divided by the number of training sequences that lead to that node.
For example, consider classifying a predictor
X is false otherwise.
Generate 100 random points and classify them:
rng(0) % For reproducibility X = rand(100,1); Y = (abs(X - .55) > .4); tree = fitctree(X,Y); view(tree,'Mode','graph')
Prune the tree:
tree1 = prune(tree,'Level',1); view(tree1,'Mode','graph')
The pruned tree correctly classifies observations
that are less than 0.15 as
true. It also correctly
classifies observations between .15 and .94 as
However, it incorrectly classifies observations that are greater than
false. Therefore the score for observations
that are greater than .15 should be about .05/.85=.06 for
and about .8/.85=.94 for
Compute the prediction scores for the first 10 rows of
[~,score] = predict(tree1,X(1:10)); [score X(1:10,:)]
ans = 0.9059 0.0941 0.8147 0.9059 0.0941 0.9058 0 1.0000 0.1270 0.9059 0.0941 0.9134 0.9059 0.0941 0.6324 0 1.0000 0.0975 0.9059 0.0941 0.2785 0.9059 0.0941 0.5469 0.9059 0.0941 0.9575 0.9059 0.0941 0.9649
every value of
X (the rightmost column) that is
less than 0.15 has associated scores (the left and center columns)
1, while the other
X have associated scores of
Find the total number of misclassifications of the Fisher iris data for a classification tree.
load fisheriris tree = fitctree(meas,species); Ypredict = resubPredict(tree); % The predictions Ysame = strcmp(Ypredict,species); % True when == sum(~Ysame) % How many are different?
ans = 3
Load Fisher's iris data set. Partition the data into training (50%)
Grow a classification tree using the all petal measurements.
Mdl = fitctree(meas(:,3:4),species); n = size(meas,1); % Sample size K = numel(Mdl.ClassNames); % Number of classes
View the classification tree.
The classification tree has four pruning levels. Level 0 is the full, unpruned tree (as displayed). Level 4 is just the root node (i.e., no splits).
Estimate the posterior probabilities for each class using the subtrees pruned to levels 1 and 3.
[~,Posterior] = resubPredict(Mdl,'SubTrees',[1 3]);
Posterior is an
K-by- 2 array of posterior probabilities. Rows of
Posterior correspond to observations, columns correspond to the classes with order
Mdl.ClassNames, and pages correspond to pruning level.
Display the class posterior probabilities for iris 125 using each subtree.
ans(:,:,1) = 0 0.0217 0.9783 ans(:,:,2) = 0 0.5000 0.5000
The decision stump (page 2 of
Posterior) has trouble predicting whether iris 125 is versicolor or virginica.