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 crossvalidation. More...  
class  F1 
F1 is a metric of performance for classification algorithms that for binary classification is equal to . More...  
class  KFoldCV 
The class KFoldCV implements kfold crossvalidation 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 , where and 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 , where and 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
enum 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

data Columnmajor data. labels Labels. callerDescription A description of the caller that can be used for error generation.
Definition at line 29 of file facilities.hpp.
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