Loss of k-nearest neighbor classifier
returns a scalar representing how well L
= loss(mdl
,tbl
,ResponseVarName
)mdl
classifies the data
in tbl
when tbl.ResponseVarName
contains the
true classifications. If tbl
contains the response variable
used to train mdl
, then you do not need to specify
ResponseVarName
.
When computing the loss, the loss
function normalizes the
class probabilities in tbl.ResponseVarName
to the class
probabilities used for training, which are stored in the Prior
property of mdl
.
The meaning of the classification loss (L
) depends on the
loss function and weighting scheme, but, in general, better classifiers yield
smaller classification loss values. For more details, see Classification Loss.
returns a scalar representing how well L
= loss(mdl
,tbl
,Y
)mdl
classifies the data
in tbl
when Y
contains the true
classifications.
When computing the loss, the loss
function normalizes the
class probabilities in Y
to the class probabilities used for
training, which are stored in the Prior
property of
mdl
.
returns a scalar representing how well L
= loss(mdl
,X
,Y
)mdl
classifies the data
in X
when Y
contains the true
classifications.
When computing the loss, the loss
function normalizes the
class probabilities in Y
to the class probabilities used for
training, which are stored in the Prior
property of
mdl
.
specifies options using one or more name-value pair arguments in addition to the
input arguments in previous syntaxes. For example, you can specify the loss function
and the classification weights.L
= loss(___,Name,Value
)
ClassificationKNN
| edge
| fitcknn
| margin