KFoldCV< MLAlgorithm, Metric, MatType, PredictionsType, WeightsType > Class Template Reference

The class KFoldCV implements k-fold cross-validation for regression and classification algorithms. More...

Public Member Functions
	KFoldCV (const size_t k, const MatType &xs, const PredictionsType &ys)
	This constructor can be used for regression algorithms and for binary classification algorithms. More...
	KFoldCV (const size_t k, const MatType &xs, const PredictionsType &ys, const size_t numClasses)
	This constructor can be used for multiclass classification algorithms. More...
	KFoldCV (const size_t k, const MatType &xs, const data::DatasetInfo &datasetInfo, const PredictionsType &ys, const size_t numClasses)
	This constructor can be used for multiclass classification algorithms that can take a data::DatasetInfo parameter. More...
	KFoldCV (const size_t k, const MatType &xs, const PredictionsType &ys, const WeightsType &weights)
	This constructor can be used for regression and binary classification algorithms that support weighted learning. More...
	KFoldCV (const size_t k, const MatType &xs, const PredictionsType &ys, const size_t numClasses, const WeightsType &weights)
	This constructor can be used for multiclass classification algorithms that support weighted learning. More...
	KFoldCV (const size_t k, const MatType &xs, const data::DatasetInfo &datasetInfo, const PredictionsType &ys, const size_t numClasses, const WeightsType &weights)
	This constructor can be used for multiclass classification algorithms that can take a data::DatasetInfo parameter and support weighted learning. More...
template<typename... MLAlgorithmArgs>
double	Evaluate (const MLAlgorithmArgs &...args)
	Run k-fold cross-validation. More...
MLAlgorithm &	Model ()
	Access and modify a model from the last run of k-fold cross-validation. More...

Detailed Description

template<typename MLAlgorithm, typename Metric, typename MatType = arma::mat, typename PredictionsType = typename MetaInfoExtractor<MLAlgorithm, MatType>::PredictionsType, typename WeightsType = typename MetaInfoExtractor<MLAlgorithm, MatType, PredictionsType>::WeightsType>
class mlpack::cv::KFoldCV< MLAlgorithm, Metric, MatType, PredictionsType, WeightsType >

The class KFoldCV implements k-fold cross-validation for regression and classification algorithms.

To construct a KFoldCV object you need to pass the k parameter and arguments that specify data. For example, you can run 10-fold cross-validation for SoftmaxRegression in the following way.

// 100-point 5-dimensional random dataset.
arma::mat data = arma::randu<arma::mat>(5, 100);
// Random labels in the [0, 4] interval.
arma::Row<size_t> labels =
    arma::randi<arma::Row<size_t>>(100, arma::distr_param(0, 4));
size_t numClasses = 5;

KFoldCV<SoftmaxRegression<>, Accuracy> cv(10, data, labels, numClasses);

double lambda = 0.1;
double softmaxAccuracy = cv.Evaluate(lambda);

Template Parameters

MLAlgorithm	A machine learning algorithm.
Metric	A metric to assess the quality of a trained model.
MatType	The type of data.
PredictionsType	The type of predictions (should be passed when the predictions type is a template parameter in Train methods of MLAlgorithm).
WeightsType	The type of weights (should be passed when weighted learning is supported, and the weights type is a template parameter in Train methods of MLAlgorithm).

Definition at line 61 of file k_fold_cv.hpp.

Constructor & Destructor Documentation

KFoldCV() [1/6]

KFoldCV	(	const size_t	k,
		const MatType &	xs,
		const PredictionsType &	ys
	)

This constructor can be used for regression algorithms and for binary classification algorithms.

Parameters

k	Number of folds (should be at least 2).
xs	Data points to cross-validate on.
ys	Predictions (labels for classification algorithms and responses for regression algorithms) for each data point.

KFoldCV() [2/6]

KFoldCV	(	const size_t	k,
		const MatType &	xs,
		const PredictionsType &	ys,
		const size_t	numClasses
	)

This constructor can be used for multiclass classification algorithms.

Parameters

k	Number of folds (should be at least 2).
xs	Data points to cross-validate on.
ys	Labels for each data point.
numClasses	Number of classes in the dataset.

KFoldCV() [3/6]

KFoldCV	(	const size_t	k,
		const MatType &	xs,
		const data::DatasetInfo &	datasetInfo,
		const PredictionsType &	ys,
		const size_t	numClasses
	)

This constructor can be used for multiclass classification algorithms that can take a data::DatasetInfo parameter.

Parameters

k	Number of folds (should be at least 2).
xs	Data points to cross-validate on.
datasetInfo	Type information for each dimension of the dataset.
ys	Labels for each data point.
numClasses	Number of classes in the dataset.

KFoldCV() [4/6]

KFoldCV	(	const size_t	k,
		const MatType &	xs,
		const PredictionsType &	ys,
		const WeightsType &	weights
	)

This constructor can be used for regression and binary classification algorithms that support weighted learning.

Parameters

k	Number of folds (should be at least 2).
xs	Data points to cross-validate on.
ys	Predictions (labels for classification algorithms and responses for regression algorithms) for each data point.
weights	Observation weights (for boosting).

KFoldCV() [5/6]

KFoldCV	(	const size_t	k,
		const MatType &	xs,
		const PredictionsType &	ys,
		const size_t	numClasses,
		const WeightsType &	weights
	)

This constructor can be used for multiclass classification algorithms that support weighted learning.

Parameters

k	Number of folds (should be at least 2).
xs	Data points to cross-validate on.
ys	Labels for each data point.
numClasses	Number of classes in the dataset.
weights	Observation weights (for boosting).

KFoldCV() [6/6]

KFoldCV	(	const size_t	k,
		const MatType &	xs,
		const data::DatasetInfo &	datasetInfo,
		const PredictionsType &	ys,
		const size_t	numClasses,
		const WeightsType &	weights
	)

This constructor can be used for multiclass classification algorithms that can take a data::DatasetInfo parameter and support weighted learning.

Parameters

k	Number of folds (should be at least 2).
xs	Data points to cross-validate on.
datasetInfo	Type information for each dimension of the dataset.
ys	Labels for each data point.
numClasses	Number of classes in the dataset.
weights	Observation weights (for boosting).

Member Function Documentation

Evaluate()

double Evaluate

(

const MLAlgorithmArgs &...

args

)

Run k-fold cross-validation.

Parameters

args	Arguments for MLAlgorithm (in addition to the passed ones in the constructor).

Model()

MLAlgorithm& Model

(

)

Access and modify a model from the last run of k-fold cross-validation.

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The documentation for this class was generated from the following file:

• src/mlpack/core/cv/k_fold_cv.hpp