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**Class: **RegressionTree

Regression error by resubstitution

`L = resubLoss(tree)`

L = resubLoss(tree,Name,Value)

L = resubLoss(tree,'Subtrees',subtreevector)

[L,se] =
resubLoss(tree,'Subtrees',subtreevector)

[L,se,NLeaf]
= resubLoss(tree,'Subtrees',subtreevector)

[L,se,NLeaf,bestlevel]
= resubLoss(tree,'Subtrees',subtreevector)

[L,...] = resubLoss(tree,'Subtrees',subtreevector,Name,Value)

returns
the resubstitution loss, meaning the loss computed for the data that `L`

= resubLoss(`tree`

)`fitrtree`

used to create `tree`

.

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

= resubLoss(`tree`

,`Name,Value`

)`Name,Value`

pair
arguments. You can specify several name-value pair arguments in any
order as `Name1,Value1,…,NameN,ValueN`

.

returns
a vector of mean squared errors for the trees in the pruning sequence `L`

= resubLoss(`tree`

,`'Subtrees'`

,subtreevector)`subtreevector`

.

`[`

returns
the vector of standard errors of the classification errors.`L`

,`se`

] =
resubLoss(`tree`

,`'Subtrees'`

,subtreevector)

`[`

returns
the vector of numbers of leaf nodes in the trees of the pruning sequence.`L`

,`se`

,`NLeaf`

]
= resubLoss(`tree`

,`'Subtrees'`

,subtreevector)

`[`

returns
the best pruning level as defined in the `L`

,`se`

,`NLeaf`

,`bestlevel`

]
= resubLoss(`tree`

,`'Subtrees'`

,subtreevector)`TreeSize`

name-value
pair. By default, `bestlevel`

is the pruning level
that gives loss within one standard deviation of minimal loss.

`[L,...] = resubLoss(`

returns
loss statistics with additional options specified by one or more `tree`

,`'Subtrees'`

,subtreevector,`Name,Value`

)`Name,Value`

pair
arguments. You can specify several name-value pair arguments in any
order as `Name1,Value1,…,NameN,ValueN`

.

`fitrtree`

| `loss`

| `resubPredict`