khoidoo/smartmachinelearning

SmartLog

Structure of history

• train_case_0:
  • param_dict
  • fold_data
    • fold_0:
    • confusion_matrix:
      • TP : int
      • FP : int
      • TN : int
      • FN : int
    • overall_analysis:
      • accuracy : float64
      • balanced_accuracy : float64
      • sensitivity : float64
      • specificity : float64
      • precision : float64
      • f1-score : float64
    • deep_analysis
      • negative_predictive_value : float64
      • false_negative_rate : float64
      • false_positive_rate : float64
      • false_discovery_rate : float64
      • false_omission_rate : float64
      • positive_likelihood_ratio : float64
      • negative_likelihood_ratio : float64
      • prevalence_threshold : float64
      • threat_score : float64
      • prevalence : float64
      • matthews_correlation_coefficient : float64
      • fowlkes_mallows_index : float64
      • informedness : float64
      • markedness : float64
      • diagnostic_odds_ratio : float64
    • ROC_CURVE : dict
      • tpr: dict
        • class1 : [tpr_values]
        • class2 : [tpr_values]
      • fpr: dict
      • thresh_holds : dict
      • fpr_micro_avg : numpy array
      • tpr_micro_avg : numpy array
      • fpr_macro_avg : numpy array
      • tpr_macro_avg : numpy array
    • AUC_SCORE
      • roc_auc_macro : float64
      • roc_auc_micro : float64
      • roc_auc_weighted : float64
    • Classification_Report
  • fold_1
  • fold_n
• train_case_1
• train_case_n