Predict class label for test data
Syntax
cpre = predict(nb,test)
cpre = predict(...,'HandleMissing',val)
Description
cpre = predict(nb,test) classifies each
row of data in test into one of the classes according
to the NaiveBayes classifier nb,
and returns the predicted class level cpre. test is
an N-by-nb.ndims matrix, where N is
the number of observations in the test data. Rows of test correspond
to points, columns of test correspond to features. cpre is
an N-by-1 vector of the same type as nb.CLevels,
and it indicates the class to which each row of test has
been assigned.
cpre = predict(...,'HandleMissing',val) specifies
how predict treats NaN (missing
values). val can be one of the following:
| 'off' (default) | Observations with NaN in any of the columns
are not classified into any class. The corresponding rows in post and logp are NaN.
The corresponding rows in cpre are NaN (if
obj.clevels is numeric or logical), empty strings
(if obj.clevels is char or cell array of strings)
or (if obj.clevels is categorical). |
| 'on' | For observations having NaN in some (but
not all) columns, post and predict computes cpre using the columns with non-NaN values.
Corresponding logp values are NaN. |
See Also
NaiveBayes, fit, posterior
 | predict (CompactTreeBagger) | | predict (TreeBagger) |  |
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