Estructura de datos tipo Pds::ClassificationMetrics.
Más...
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| namespace | Pds |
| | Nombre de espacio para Pds (Procesamiento Digital de Senales)
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Estructura de datos tipo Pds::ClassificationMetrics.
#include <Pds/ClassificationMetrics>
Informacion adicional puede ser encontrada en [3]
◆ ClassificationMetrics()
| Pds::ClassificationMetrics::ClassificationMetrics |
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void |
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Crea un objeto vazio de tipo Pds::ClassificationMetrics.
Dat.Precision = 0;
Dat.Recall = 0;
Dat.Accuracy = 0;
Dat.FScore = 0;
Dat.Threshold = 0;
◆ Calculate()
| static Pds::ClassificationMetrics Pds::ClassificationMetrics::Calculate |
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double |
Threshold, |
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const Pds::Vector & |
Ypredict, |
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const Pds::Vector & |
Yactual |
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) |
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static |
Crea un objeto Dat de tipo Pds::ClassificationMetrics.
TP: True positive.
FP: False positive.
TN: True negative.
FN: False negative.
![\[ \begin{array}{lll} Dat.Precision & = & \frac{TP}{TP+FP};\\ \\ Dat.Recall & = & \frac{TP}{TP+FN};\\ \\ Dat.Accuracy & = & \frac{TN+TP}{N}; \\ \\ Dat.FScore & = & \frac{2.0~Dat.Precision~Dat.Recall}{Dat.Precision+Dat.Recall}; \\ \\ Dat.Threshold & = & Threshold; \\ \end{array} \]](form_0.png)
- Parámetros
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| [in] | Threshold | Umbral para el vector Ypredict. |
| [in] | Ypredict | Vector de datos predecidos. Internamente Ypredict>=Threshold. |
| [in] | Yactual | Vector de datos verdaderos. Internamente Ypredict>=0.5. |
- Devuelve
- Un objeto
de tipo Pds::ClassificationMetrics.
- Ejemplos
- example_decisiontree.cpp, example_decisiontree_simple.cpp, example_decisiontree_simple2.cpp, example_ensembleperceptron.cpp, example_kmeansnn.cpp, example_kmeansperceptron.cpp, example_logisticregression_ce.cpp, example_logisticregression_ms.cpp, example_logisticregression_svm.cpp, example_mncknn.cpp, example_perceptron_kernel_fourier_yinyang.cpp, example_perceptron_kernel_gaussian_circle.cpp, example_perceptron_kernel_gaussian_gmm.cpp, example_perceptron_kernel_gaussian_yinyang.cpp, example_perceptron_kernel_gaussian_yinyang2.cpp, example_perceptron_kernel_poly_band.cpp, example_perceptron_kernel_poly_circle.cpp, example_perceptron_kernel_poly_sine.cpp y example_perceptron_predict.cpp.
◆ Optimal()
| static Pds::ClassificationMetrics Pds::ClassificationMetrics::Optimal |
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const Pds::Vector & |
Ypredict, |
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const Pds::Vector & |
Yactual |
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) |
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static |
◆ Print()
| void Pds::ClassificationMetrics::Print |
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std::string |
str = "" | ) |
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Imprime en pantalla los datos de la estructura tipo Pds::ClassificationMetrics.
Pds::ClassificationMetrics Metrics;
// Working data Y and Ypr;
Metrics.Optimal(Ypr,Y);
Metrics.Print("\nTraining metrics\n");
- Parámetros
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| [in] | str | Cadena de texto a mostrar antes de imprimir los datos. |
- Ejemplos
- example_decisiontree.cpp, example_decisiontree_simple.cpp, example_decisiontree_simple2.cpp, example_ensembleperceptron.cpp, example_kmeansnn.cpp, example_kmeansperceptron.cpp, example_logisticregression_ce.cpp, example_logisticregression_ms.cpp, example_logisticregression_svm.cpp, example_mncknn.cpp, example_perceptron_kernel_fourier_yinyang.cpp, example_perceptron_kernel_gaussian_circle.cpp, example_perceptron_kernel_gaussian_gmm.cpp, example_perceptron_kernel_gaussian_yinyang.cpp, example_perceptron_kernel_gaussian_yinyang2.cpp, example_perceptron_kernel_poly_band.cpp, example_perceptron_kernel_poly_circle.cpp, example_perceptron_kernel_poly_sine.cpp y example_perceptron_predict.cpp.
◆ ~ClassificationMetrics()
| Pds::ClassificationMetrics::~ClassificationMetrics |
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| ) |
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![\[ \begin{array}{lll} Dat.Precision & = & \frac{TP}{TP+FP};\\ \\ Dat.Recall & = & \frac{TP}{TP+FN};\\ \\ Dat.Accuracy & = & \frac{TN+TP}{N}; \\ \\ Dat.FScore & = & \frac{2.0~Dat.Precision~Dat.Recall}{Dat.Precision+Dat.Recall}; \\ \\ Dat.Threshold & = & El~valor~que~provoca~el~mayor~Fscore; \\ \end{array} \]](form_2.png)
1.9.2