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...
Static Public Member Functions  
template < typename MLAlgorithm , typename DataType >  
static double  Evaluate (MLAlgorithm &model, const DataType &data, const arma::Row< size_t > &labels) 
Run classification and calculate recall. More...  
Static Public Attributes  
static const bool  NeedsMinimization = false 
Information for hyperparameter tuning code. More...  
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.
For multiclass classification the recall metric can be used with the following strategies for averaging.
where and are the numbers of true positives and false negatives respectively for the class (label) .
where and are the numbers of true positives and false negatives respectively for the class (label) .
AS  An average strategy. 
PositiveClass  In the case of binary classification (AS = Binary) positives are assumed to have labels equal to this value. 
Definition at line 48 of file recall.hpp.

static 
Run classification and calculate recall.
model  A classification model. 
data  Columnmajor data containing test items. 
labels  Ground truth (correct) labels for the test items. 

static 
Information for hyperparameter tuning code.
It indicates that we want to maximize the metric.
Definition at line 67 of file recall.hpp.