Compute posterior probability of each class for test data
Syntax
post = posterior(nb,test)
[post,cpre] = posterior(nb,test)
[post,cpre,logp] = posterior(nb,test)
[...] = posterior(..., 'HandleMissing',val)
Description
post = posterior(nb,test) returns the posterior
probability of the observations in test according
to the NaiveBayes object nb. test is
a 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. post is
a N-by-nb.nclasses matrix containing
the posterior probability of each observation for each class. post(i,j) is
the posterior probability of point I belonging to class j.
Classes are ordered the same as nb.clevels, i.e.,
column j of post corresponds
to the jth class in nb.clevels.
The posterior probabilities corresponding to any empty classes
are NaN.
[post,cpre] = posterior(nb,test) returns cpre,
an N-by-1 vector, containing the class to which
each row of test has been assigned. cpre has
the same type as nb.CLevels.
[post,cpre,logp] = posterior(nb,test) returns logp,
an N-by-1 vector containing estimates of the log
of the probability density function (PDF). logp(i) is
the log of the PDF of point i. The PDF value of
point i is the sum of Prob(point
I | class J) * Pr{class J} taken over all classes.
[...] = posterior(..., 'HandleMissing',val) specifies
how posterior 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 cpre are
computed using the columns with non-NaN values.
Corresponding logp values are NaN. |
See Also
NaiveBayes, fit, predict
 | posterior (gmdistribution) | | prctile |  |
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