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...
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 precision. More...  
Static Public Attributes  
static const bool  NeedsMinimization = false 
Information for hyperparameter tuning code. More...  
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.
For multiclass classification the precision metric can be used with the following strategies for averaging.
where and are the numbers of true positives and false positives respectively for the class (label) .
where and are the numbers of true positives and false positives 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 precision.hpp.

static 
Run classification and calculate precision.
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 precision.hpp.