F1 is a metric of performance for classification algorithms that for binary classification is equal to . 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 F1. More...  
Static Public Attributes  
static const bool  NeedsMinimization = false 
Information for hyperparameter tuning code. More...  
F1 is a metric of performance for classification algorithms that for binary classification is equal to .
For multiclass classification the F1 metric can be used with the following strategies for averaging.
In the case of multiclass classification it is assumed that there are instances of every label from 0 to max(labels) among input data points.
The returned value for F1 will be zero if both precision and recall turn out to be zeros.
AS  An average strategy. 
PositiveClass  In the case of binary classification (AS = Binary) positives are assumed to have labels equal to this value. 

static 
Run classification and calculate F1.
model  A classification model. 
data  Columnmajor data containing test items. 
labels  Ground truth (correct) labels for the test items. 

static 