Predict labels using classification tree
uses
additional options specified by one or more label
= predict(Mdl
,X
,Name,Value
)Name,Value
pair
arguments. For example, you can specify to prune Mdl
to
a particular level before predicting labels.
[
uses any of the input argument
in the previous syntaxes and additionally returns:label
,score
,node
,cnum
]
= predict(___)
A matrix of classification scores (score
)
indicating the likelihood that a label comes from a particular class.
For classification trees, scores are posterior probabilities. For
each observation in X
, the predicted class label corresponds
to the minimum expected
misclassification cost among all classes.
A vector of predicted node numbers for the classification
(node
).
A vector of predicted class number for the classification
(cnum
).
predict
generates predictions by following
the branches of Mdl
until it reaches a leaf node
or a missing value. If predict
reaches a leaf node,
it returns the classification of that node.
If predict
reaches a node with a missing value
for a predictor, its behavior depends on the setting of the Surrogate
name-value
pair when fitctree
constructs Mdl
.
Surrogate
= 'off'
(default)
— predict
returns the label with the largest
number of training samples that reach the node.
Surrogate
= 'on'
— predict
uses
the best surrogate split at the node. If all surrogate split variables
with positive predictive measure of association are
missing, predict
returns the label with the largest
number of training samples that reach the node. For a definition,
see Predictive Measure of Association.
To integrate the prediction of a classification tree model into Simulink®, you can use the ClassificationTree
Predict block in the Statistics and Machine Learning Toolbox™ library or a MATLAB® Function block with the predict
function. For
examples, see Predict Class Labels Using ClassificationTree Predict Block and Predict Class Labels Using MATLAB Function Block.
When deciding which approach to use, consider the following:
If you use the Statistics and Machine Learning Toolbox library block, you can use the Fixed-Point Tool (Fixed-Point Designer) to convert a floating-point model to fixed point.
Support for variable-size arrays must be enabled for a MATLAB Function block with the predict
function.
If you use a MATLAB Function block, you can use MATLAB functions for preprocessing or post-processing before or after predictions in the same MATLAB Function block.
ClassificationTree
| compact
| CompactClassificationTree
| edge
| fitctree
| loss
| margin
| prune