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 single 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. 
For ensembles, a classification score represents the confidence of a classification into a class. The higher the score, the higher the confidence.
Different ensemble algorithms have different definitions for their scores. Furthermore, the range of scores depends on ensemble type. For example:
AdaBoostM1
scores range from –∞
to ∞.
Bag
scores range from 0
to 1
.