Classify observations using ensemble of classification models
labels = predict(Mdl,X)
labels = predict(Mdl,X,Name,Value)
[labels,score]
= predict(___)
uses
additional options specified by one or more labels
= predict(Mdl
,X
,Name,Value
)Name,Value
pair
arguments.
[
also returns a matrix of classification scores (labels
,score
]
= predict(___)score
), indicating
the likelihood that a label comes from a particular class, using any
of the input arguments in the previous syntaxes. For each observation
in X
, the predicted class label corresponds to
the maximum score among all classes.

A classification ensemble created by 

Predictor data to be classified, specified as a numeric matrix or table. Each row of

Specify optional
commaseparated pairs of Name,Value
arguments. Name
is
the argument name and Value
is the corresponding value.
Name
must appear inside quotes. You can specify several name and value
pair arguments in any order as
Name1,Value1,...,NameN,ValueN
.

Indices of weak learners Default: 

A logical matrix of size When Default: 

Vector of classification labels. 

A matrix with one row per observation and one column per class.
For each observation and each class, the score generated by each tree
is the probability of this observation originating from this class
computed as the fraction of observations of this class in a tree leaf. 
ClassificationBaggedEnsemble
 ClassificationEnsemble
 CompactClassificationEnsemble
 edge
 fitcensemble
 loss
 margin