LeaveOneOutSplitter

class LeaveOneOutSplitter(*args)

Leave-one-out splitter.

Generates train/test indices to split samples in train/test sets. Each sample is used once as a test set while the remaining samples form the training set.

Parameters:

Nint

Size of the set of indices in which the indices are chosen

Examples

>>> import openturns as ot
>>> X = ot.Normal().getSample(10)
>>> splitter = ot.LeaveOneOutSplitter(X.getSize())
>>> for indicesTrain, indicesTest in splitter:
...     XTrain, XTest = X[indicesTrain], X[indicesTest]

Methods

getClassName()	Accessor to the object's name.
getN()	Set size accessor.
getName()	Accessor to the object's name.
getSize()	Number of sets generated.
hasName()	Test if the object is named.
setName(name)	Accessor to the object's name.

__init__(*args)

getClassName()

Accessor to the object’s name.

Returns:

class_namestr

The object class name (object.__class__.__name__).

getN()

Set size accessor.

Returns:

Nint

Size of the set of indices in which the indices are chosen

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

getSize()

Number of sets generated.

Returns:

lengthint

Number of sets of indices generated.

hasName()

Test if the object is named.

Returns:

hasNamebool

True if the name is not empty.

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.

Examples using the class

Polynomial chaos expansion cross-validation

Polynomial chaos expansion cross-validation

Compute leave-one-out error of a polynomial chaos expansion

Compute leave-one-out error of a polynomial chaos expansion