Classification error for naive Bayes classifier

returns the minimum classification loss (see Classification Loss), a
scalar representing how well the trained naive Bayes classifier
`L`

= loss(`Mdl`

,`tbl`

,`ResponseVarName`

)`Mdl`

classifies the predictor data in table
`tbl`

) as compared to the true class labels in
`tbl.ResponseVarName`

.

`loss`

normalizes the class probabilities in
`tbl.ResponseVarName`

to the prior class probabilities
`fitcnb`

used for training, stored
in the `Prior`

property of `Mdl`

.

returns the minimum classification loss (`L`

= loss(`Mdl`

,`tbl`

,`Y`

)`L`

), a scalar
representing how well the trained naive Bayes classifier
`Mdl`

classifies the predictor data in table
`tbl`

) as compared to the true class labels in
`Y`

.

`loss`

normalizes the class probabilities in
`Y`

to the prior class probabilities `fitcnb`

used for training, stored
in the `Prior`

property of `Mdl`

.

returns the minimum classification loss (`L`

= loss(`Mdl`

,`X`

,`Y`

)`L`

), a scalar
representing how well the trained naive Bayes classifier
`Mdl`

classifies the predictor data
(`X`

) as compared to the true class labels
(`Y`

).

`loss`

normalizes the class probabilities in
`Y`

to the prior class probabilities `fitcnb`

used for training, stored
in the `Prior`

property of `Mdl`

.

returns the classification loss with additional options specified by one or more
`L`

= loss(___,`Name,Value`

)`Name,Value`

pair arguments, using any of the previous
syntaxes.

[1] Hastie, T., R. Tibshirani, and J. Friedman. *The
Elements of Statistical Learning*, second edition. Springer, New York,
2008.

`ClassificationNaiveBayes`

| `CompactClassificationNaiveBayes`

| `fitcnb`

| `predict`

| `resubLoss`