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clase:iabd:pia:2eval:tema07.metricas_derivadas [2024/03/23 10:52]
admin [Métricas mixtas] 		clase:iabd:pia:2eval:tema07.metricas_derivadas [2024/03/25 14:33]
admin [Juntado dos Métricas Básicas]
Línea 173: Línea 173:
   * [[https://keras.io/api/metrics/classification_metrics/#auc-class|AUC class]]   * [[https://keras.io/api/metrics/classification_metrics/#auc-class|AUC class]]
   * [[https://aprendeia.com/curvas-roc-y-area-bajo-la-curva-auc-machine-learning/|Curvas ROC y Área bajo la curva (AUC)]]   * [[https://aprendeia.com/curvas-roc-y-area-bajo-la-curva-auc-machine-learning/|Curvas ROC y Área bajo la curva (AUC)]]
-  * [[https://towardsdatascience.com/an-understandable-guide-to-roc-curves-and-auc-and-why-and-when-to-use-them-92020bc4c5c1|An Understandable Guide to ROC Curves And AUC and Why and When to use them?]] 
-  * [[https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5|Understanding AUC - ROC Curve]] 
   * [[https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-classification-in-python/|How to Use ROC Curves and Precision-Recall Curves for Classification in Python]]   * [[https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-classification-in-python/|How to Use ROC Curves and Precision-Recall Curves for Classification in Python]]
-  * {{ :clase:iabd:pia:2eval:receiver_operating_characteristic_roc_curves._an_analysis_tool_for_detection_performance.pdf |Receiver Operating Characteristic (ROC) Curves: An Analysis Tool for Detection Performance}} 
   * {{ :clase:iabd:pia:2eval:predicting_receiver_operating_characteristic_curve_area_under_curve_and_arithmetic_means_of_accuracies_based_on_the_distribution_of_data_samples.pdf |Predicting Receiver Operating Characteristic curve, area under curve , and arithmetic means of accuracies based on the distribution of data samples}}   * {{ :clase:iabd:pia:2eval:predicting_receiver_operating_characteristic_curve_area_under_curve_and_arithmetic_means_of_accuracies_based_on_the_distribution_of_data_samples.pdf |Predicting Receiver Operating Characteristic curve, area under curve , and arithmetic means of accuracies based on the distribution of data samples}}
-  * {{ :clase:iabd:pia:2eval:a_variable_selection_method_for_multiclass_classification_problems_using_two-class_roc_analysis.pdf |A variable selection method for multiclass classification problems using two-class ROC analysis}} 
   * [[https://neptune.ai/blog/f1-score-accuracy-roc-auc-pr-auc|F1 Score vs ROC AUC vs Accuracy vs PR AUC: Which Evaluation Metric Should You Choose?]]   * [[https://neptune.ai/blog/f1-score-accuracy-roc-auc-pr-auc|F1 Score vs ROC AUC vs Accuracy vs PR AUC: Which Evaluation Metric Should You Choose?]]
   * [[https://stackoverflow.com/questions/44172162/f1-score-vs-roc-auc|F1 Score vs ROC AUC]]    * [[https://stackoverflow.com/questions/44172162/f1-score-vs-roc-auc|F1 Score vs ROC AUC]] 
clase/iabd/pia/2eval/tema07.metricas_derivadas.txt · Última modificación: 2024/03/25 14:47 por admin

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