RegressionPartitionedModel is a set of regression
models trained on cross-validated 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 in-fold observations to predict response for
out-of-fold 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.
Construction
cvmodel =
crossval(tree) creates a cross-validated
classification model from a regression tree. For syntax details, see
the crossval method
reference page.
cvmodel = fitrtree(X,Y,Name,Value) creates
a cross-validated model when name is one of 'CrossVal', 'KFold', 'Holdout', 'Leaveout',
or 'CVPartition'. For syntax details, see the fitrtree function reference page.
List of categorical predictors. CategoricalPredictors is
a numeric vector with indices from 1 to p,
where p is the number of columns of X.
CrossValidatedModel
Name of the cross-validated model, a string.
Kfold
Number of folds used in a cross-validated tree, a positive integer.
ModelParameters
Object holding parameters of tree.
Partition
The partition of class cvpartition used in the cross-validated
model.
PredictorNames
A cell array of names for the predictor variables, in the order
in which they appear in X.
ResponseName
Name of the response variable Y, a string.
ResponseTransform
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 'none' means @(x)x,
or no transformation.
Add or change a ResponseTransform function
using dot notation:
ctree.ResponseTransform = @function
Trained
The trained learners, a cell array of compact regression models.
W
The scaled weights, a vector with length n,
the number of rows in X.
X
A matrix of predictor values. Each column of X represents
one variable, and each row represents one observation.
Y
A numeric column vector with the same number of rows as X.
Each entry in Y is the response to the data in
the corresponding row of X.