F1 is a metric of performance for classification algorithms that for binary classification is equal to 
#include <f1.hpp>
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 hyper-parameter tuning code. More... | |
Detailed Description
template<AverageStrategy AS, size_t PositiveClass = 1>
class mlpack::cv::F1< AS, PositiveClass >
class mlpack::cv::F1< AS, PositiveClass >
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.
- Micro. The result is calculated by the above formula, but microaveraged precision and microaveraged recall are used.
- Macro. F1 is calculated for each class (with values used for calculation of macroaveraged precision and macroaveraged recall), and then the F1 values are averaged.
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.
- Template Parameters
-
AS An average strategy. PositiveClass In the case of binary classification (AS = Binary) positives are assumed to have labels equal to this value.
Member Function Documentation
◆ Evaluate()
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static |
Run classification and calculate F1.
- Parameters
-
model A classification model. data Column-major data containing test items. labels Ground truth (correct) labels for the test items.
Member Data Documentation
◆ NeedsMinimization
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static |
The documentation for this class was generated from the following file:
- src/mlpack/core/cv/metrics/f1.hpp
