Crossvalidated regression model
RegressionPartitionedModel
is a set of regression
models trained on crossvalidated folds. Estimate the quality of regression
by cross validation using one or more "kfold" methods: kfoldPredict
, kfoldLoss
,
and kfoldfun
. Every "kfold" method
uses models trained on infold observations to predict response for
outoffold observations. For example, suppose you cross validate
using five folds. In this case, every training fold contains roughly
4/5 of the data and every test fold contains roughly 1/5 of the data.
The first model stored in Trained{1}
was trained
on X
and Y
with the first 1/5
excluded, the second model stored in Trained{2}
was
trained on X
and Y
with the
second 1/5 excluded, and so on. When you call kfoldPredict
,
it computes predictions for the first 1/5 of the data using the first
model, for the second 1/5 of data using the second model and so on.
In short, response for every observation is computed by kfoldPredict
using
the model trained without this observation.
creates a crossvalidated
classification model from a regression tree. For syntax details, see
the cvmodel
=
crossval(tree
)crossval
method
reference page.
cvmodel = fitrtree(X,Y,Name,Value)
creates
a crossvalidated model when name
is one of 'CrossVal'
, 'KFold'
, 'Holdout'
, 'Leaveout'
,
or 'CVPartition'
. For syntax details, see the fitrtree
function reference page.

A regression tree constructed with 

List of categorical predictors. 

Name of the crossvalidated model, a string. 

Number of folds used in a crossvalidated tree, a positive integer. 

Object holding parameters of 

The partition of class 

A cell array of names for the predictor variables, in the order
in which they appear in 

Name of the response variable 

Function handle for transforming the raw response values (mean
squared error). The function handle should accept a matrix of response
values and return a matrix of the same size. The default string Add or change a ctree.ResponseTransform = @function 

The trained learners, a cell array of compact regression models. 

The scaled 

A matrix of predictor values. Each column of 

A numeric column vector with the same number of rows as 
kfoldfun  Cross validate function 
kfoldLoss  Crossvalidation loss of partitioned regression model 
kfoldPredict  Predict response for observations not used for training. 
Value. To learn how value classes affect copy operations, see Copying Objects in the MATLAB^{®} documentation.