mlpack  3.0.2
mlpack::cv Namespace Reference

Classes

class  Accuracy
 The Accuracy is a metric of performance for classification algorithms that is equal to a proportion of correctly labeled test items among all ones for given test items. More...

 
class  CVBase
 An auxiliary class for cross-validation. More...

 
class  F1
 F1 is a metric of performance for classification algorithms that for binary classification is equal to $ 2 * precision * recall / (precision + recall) $. More...

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

 
class  MetaInfoExtractor
 MetaInfoExtractor is a tool for extracting meta information about a given machine learning algorithm. More...

 
class  MSE
 The MeanSquaredError is a metric of performance for regression algorithms that is equal to the mean squared error between predicted values and ground truth (correct) values for given test items. More...

 
struct  NotFoundMethodForm
 
class  Precision
 Precision is a metric of performance for classification algorithms that for binary classification is equal to $ tp / (tp + fp) $, where $ tp $ and $ fp $ are the numbers of true positives and false positives respectively. More...

 
class  Recall
 Recall is a metric of performance for classification algorithms that for binary classification is equal to $ tp / (tp + fn) $, where $ tp $ and $ fn $ are the numbers of true positives and false negatives respectively. More...

 
struct  SelectMethodForm
 A type function that selects a right method form. More...

 
struct  SelectMethodForm< MLAlgorithm >
 
struct  SelectMethodForm< MLAlgorithm, HasMethodForm, HMFs... >
 
class  SimpleCV
 SimpleCV splits data into two sets - training and validation sets - and then runs training on the training set and evaluates performance on the validation set. More...

 
struct  TrainForm
 A wrapper struct for holding a Train form. More...

 
struct  TrainForm< MT, PT, void, false, false >
 
struct  TrainForm< MT, PT, void, false, true >
 
struct  TrainForm< MT, PT, void, true, false >
 
struct  TrainForm< MT, PT, void, true, true >
 
struct  TrainForm< MT, PT, WT, false, false >
 
struct  TrainForm< MT, PT, WT, false, true >
 
struct  TrainForm< MT, PT, WT, true, false >
 
struct  TrainForm< MT, PT, WT, true, true >
 
struct  TrainFormBase
 

Enumerations

enum  AverageStrategy
{
  Binary
,
  Micro
,
  Macro

}
 This enum declares possible strategies for averaging that can be used in some metrics like precision, recall, and F1. More...

 

Functions

template
<
typename
DataType
>
void AssertSizes (const DataType &data, const arma::Row< size_t > &labels, const std::string &callerDescription)
 Assert there is the same number of the given data points and labels. More...

 

Enumeration Type Documentation

◆ AverageStrategy

This enum declares possible strategies for averaging that can be used in some metrics like precision, recall, and F1.

The "Binary" strategy means binary classification is going to be used, and there is no need to average.

Enumerator
Binary 
Micro 
Macro 

Definition at line 25 of file average_strategy.hpp.

Function Documentation

◆ AssertSizes()

void mlpack::cv::AssertSizes ( const DataType &  data,
const arma::Row< size_t > &  labels,
const std::string &  callerDescription 
)

Assert there is the same number of the given data points and labels.

Parameters
dataColumn-major data.
labelsLabels.
callerDescriptionA description of the caller that can be used for error generation.

Definition at line 29 of file facilities.hpp.