lucid::KFold Class Reference

Generic K-Fold cross-validation, where the dataset is divided into k folds. More...

#include <KFold.h>

Inheritance diagram for lucid::KFold:

Public Member Functions
	KFold (Dimension num_folds=5, bool shuffle=true)
	Construct a new KFold cross-validator with the specified number of folds.
Dimension	num_folds (ConstMatrixRef training_inputs) const override
	Get the number of folds used in cross-validation.
Dimension	num_folds () const
	Get read-only access to the num_folds of the the cross-validation.
bool	shuffle () const
	Check whether the to be shuffled before being split into folds is data.
std::string	to_string () const override
	Obtain the string representation of this object.
Public Member Functions inherited from lucid::CrossValidator
double	fit (Estimator &estimator, ConstMatrixRef training_inputs, ConstMatrixRef training_outputs, const scorer::Scorer &scorer=nullptr) const
	Fit the estimator using cross-validation on the provided training_inputs and training_outputs.
double	fit (Estimator &estimator, ConstMatrixRef training_inputs, ConstMatrixRef training_outputs, const Tuner &tuner, const scorer::Scorer &scorer=nullptr) const
	Fit the estimator using cross-validation on the provided training_inputs and training_outputs using the tuner tuner to optimize @hp.
std::vector< double >	score (const Estimator &estimator, ConstMatrixRef inputs, ConstMatrixRef outputs, const scorer::Scorer &scorer=nullptr) const
	Evaluate the estimator using cross-validation on the provided inputs and outputs.

Private Member Functions
std::pair< SliceSelector, SliceSelector >	compute_folds (ConstMatrixRef training_inputs) const override
	Compute the training and validation folds for cross-validation.

Private Attributes
Dimension	num_folds_
	Number of folds to use in cross-validation.
bool	shuffle_
	Whether to shuffle the data before splitting into folds.

Additional Inherited Members
Public Types inherited from lucid::CrossValidator
using	SliceSelector = std::vector<std::vector<Index>>
	Type alias for slice selector.
Protected Member Functions inherited from lucid::CrossValidator
double	fit (Estimator &estimator, ConstMatrixRef training_inputs, ConstMatrixRef training_outputs, const Tuner *tuner, const scorer::Scorer &scorer) const
	Fit the estimator using cross-validation on the provided training_inputs and training_outputs using the tuner tuner to optimize @hp, if provided.

Detailed Description

Generic K-Fold cross-validation, where the dataset is divided into k folds.

The model is trained k times, each time using k-1 folds for training and the remaining fold for validation. The final model is the one that achieved the best validation score across all folds. This technique helps to mitigate overfitting and provides a more robust estimate of the model's performance.

---
title: "K fold [k = 4]"
config:
    packet:
        bitWidth: 64
        bitsPerRow: 12
        showBits: false
---
packet
+9: "Training"
+3: "Validation"
+6: "Training"
+3: "Validation"
+3: "Training"
+3: "Training"
+3: "Validation"
+6: "Training"
+3: "Validation"
+9: "Training"

Constructor & Destructor Documentation

KFold()

lucid::KFold::KFold ( Dimension num_folds = 5, bool shuffle = true )

explicit

Construct a new KFold cross-validator with the specified number of folds.

Precondition

num_folds must be at least 2

Parameters

num_folds	number of folds to use in cross-validation
shuffle	whether to shuffle the data before splitting into folds

Member Function Documentation

compute_folds()

std::pair< CrossValidator::SliceSelector, CrossValidator::SliceSelector > lucid::KFold::compute_folds ( ConstMatrixRef training_inputs ) const

nodiscardoverrideprivatevirtual

Compute the training and validation folds for cross-validation.

Each fold is represented as a pair of index vectors, where the first vector contains the indices for the training set and the second vector contains the indices for the validation set.

Parameters

training_inputs

\( \texttip{n}{Number of samples} \times \texttip{d_x}{Dimension of the input vector space} \) training input data

Returns

pair of slice selectors for training and validation folds

Implements lucid::CrossValidator.

num_folds() [1/2]

Dimension lucid::KFold::num_folds ( ) const

inlinenodiscard

Get read-only access to the num_folds of the the cross-validation.

Returns

num_folds of the the cross-validation

num_folds() [2/2]

Dimension lucid::KFold::num_folds ( ConstMatrixRef training_inputs ) const

nodiscardoverridevirtual

Get the number of folds used in cross-validation.

In KFold cross-validation, the dataset is divided into the number of folds specified during construction.

Precondition

The number of samples in training_inputs must be at least equal to the number of folds

Parameters

training_inputs

\( \texttip{n}{Number of samples} \times \texttip{d_x}{Dimension of the input vector space} \) training input data

Returns

number of folds

Implements lucid::CrossValidator.

shuffle()

bool lucid::KFold::shuffle ( ) const

inlinenodiscard

Check whether the to be shuffled before being split into folds is data.

Returns

true if the to be shuffled before being split into folds is data

false if the to be shuffled before being split into folds is not data

to_string()

std::string lucid::KFold::to_string ( ) const

nodiscardoverridevirtual

Obtain the string representation of this object.

Returns

string representation of this object

Reimplemented from lucid::CrossValidator.

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

• lucid/model/KFold.h
• lucid/model/KFold.cpp