# Machine Learning for Bankruptcy Prediction in the American Stock Market: Dataset and Benchmarks
## 8 Results

Table 4: _Cont._  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \multicolumn{11}{c}{} & \multicolumn{11}{c}{**WL = 2**} \\ \hline  & **TP** & **FN** & **FP** & **TN** & **AUC Score** & **Micro-f1** & **Macor-f1** & **I Error** & **Rec Rankuppucky** & **Pr Rankuppucky** & **Rec Healthy** & **Pr Healthy** \\ \hline
**Svm** & 51 & 52 & 759 & 2284 & 0.627 & 0.75 & 0.483 & 24.149 & 50.485 & 0.495 & 0.063 & 0.759 & 0.979 \\
**Ann** & 74 & 29 & 842 & 2201 & 0.819 & 0.732 & 0.483 & 26.79 & 28.155 & 0.718 & 0.081 & 0.732 & 0.988 \\
**Logistic Regression** & 85 & 18 & 1082 & 201 & 0.74 & 0.661 & 0.462 & 34.265 & 17.476 & 0.825 & 0.073 & 0.656 & 0.991 \\
**AdaBoost** & 83 & 20 & 1042 & 2101 & 0.737 & 0.673 & 0.467 & 33.153 & 19.417 & 0.806 & 0.074 & 0.668 & 0.991 \\
**Random Forest** & 78 & 25 & 853 & 2200 & 0.743 & 0.73 & 0.495 & 27.144 & 24.272 & 0.757 & 0.084 & 0.729 & 0.999 \\
**Gradient Boosting** & 81 & 22 & 1002 & 2141 & 0.734 & 0.685 & 0.472 & 31.88 & 21.359 & 0.786 & 0.075 & 0.681 & 0.99 \\
**XgBoost** & 79 & 24 & 946 & 2197 & 0.733 & 0.701 & 0.48 & 30.099 & 23.301 & 0.767 & 0.077 & 0.699 & 0.989 \\ \hline \multicolumn{11}{c}{} & \multicolumn{11}{c}{**WL = 3**} \\ \hline  & **TP** & **FN** & **FP** & **TN** & **AUC Score** & **Micro-f1** & **Macor-f1** & **Error** & **H Error** & **Rec Rankuppucky** & **Pr Rankuppucky** & **Rec Healthy** & **Pr Healthy** \\ \hline
**Svm** & 48 & 53 & 554 & 2452 & 0.645 & 0.805 & 0.513 & 18.43 & 52.475 & 0.475 & 0.08 & 0.816 & 0.979 \\
**Ann** & 83 & 18 & 779 & 2227 & 0.872 & 0.743 & 0.51 & 25.915 & 17.822 & 0.822 & 0.096 & 0.741 & 0.992 \\
**AdaBoost** \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline  & **TP** & **FN** & **FP** & **TN** & **AUC Source** & **Micro-fl** & **Micro-fl** & **1 Error** & **1 Error** & **Rec Rankpuctively** & **Pr Rankpuctively** & **Rec Healthy** & **Pr Healthy** \\ \hline
**Svm** & 46 & 50 & 750 & 2074 & 0.607 & 0.726 & 0.471 & 26.558 & 52.033 & 0.479 & 0.058 & 0.734 & 0.976 \\
**Ann** & 69 & 27 & 705 & 2119 & 0.811 & 0.749 & 0.506 & 24.965 & 28.125 & 0.719 & 0.089 & 0.75 & 0.987 \\
**Logistic Regression** & 77 & 19 & 742 & 2082 & 0.77 & 0.779 & 0.507 & 28.725 & 19.972 & 0.002 & 0.094 & 0.737 & 0.981 \\
**AdaBoost** & 71 & 25 & 844 & 1800 & 0.72 & 0.702 & 0.48 & 29.887 & 26.042 & 0.74 & 0.078 & 0.701 & 0.988 \\
**Random Forest** & 81 & 15 & 770 & 2054 & 0.786 & 0.731 & 0.505 & 27.266 & 15.525 & 0.844 & 0.095 & 0.727 & 0.993 \\
**Cardient Boosting** & 77 & 17 & 880 & 1944 & 0.756 & 0.603 & 0.841 & 31.161 & 17.078 & 0.832 & 0.052 & 0.688 & 0.991 \\
**Xgboost** & 77 & 19 & 824 & 2000 & 0.755 & 0.711 & 0.49 & 29.178 & 19.792 & 0.802 & 0.085 & 0.708 & 0.991 \\ \hline \multicolumn{11}{c}{**WL = 5**} \\ \hline  & **TP** & **FN** & **FP** & **TN** & **AUC Source** & **Micro-fl** & **Macro-fl** & **1 Error** & **1 Error** & **Rec Rankpuctively** & **Pr Rankpuctively** & **Rec Healthy** & **Pr Healthy** \\ \hline
**Svm** & 62 & 29 & 539 & 2105 & 0.799 & 0.792 & 0.530 & 20.386 & 31.668 & 0.681 & 0.103 & 0.796 & 0.986 \\
**Ann** & 85 & 6 & 1270 & 1374 & 0.862 & 0.533 & 0.4 & 48.033 & 6.953 & 0.954 & 0.063 & 0.52 & 0.996 \\
**Logistic Regression** & 78 & 13 & 869 & 1795 & 0.768 & 0.685 & 0.48 & 32.11 & 14.226 & 0.857 & 0.084 & 0.659 & 0.993 \\
**AdaBoost** & 74 & 17 & 953 & 1691 & 0.726 & 0.645 & 0.356 & 36.048 & 18.681 & 0.033 & 0.072 & 0.64 & 0.99 \\
**Random Forest** & 79 & 12 & 715 & 1929 & 0.799 & 0.734 & 0.51 & 27.042 & 13.187 & 0.688 & 0.099 & 0.73 & 0.994 \\
**Gradient Boosting** & 74 & 17 & 866 & 1778 & 0.743 & 0.657 & 0.472 & 32.753 & 18.681 & 0.031 & 0.079 & 0.672 & 0.991 \\
**Xgboost** & 73 & 18 & 610 & 2034 & 0.786 & 0.77 & 0.527 & 23.071 & 19.78 & 0.802 & 0.107 & 0.709 & 0.991 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cont.